Comparative Binding Affinity Prediction for VEGFR-2 Inhibitors: Integrating Computational Models and Experimental Validation

Abstract

This article provides a comprehensive analysis of computational methodologies for predicting the binding affinity of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) inhibitors, a critical target in anti-angiogenic cancer therapy. Aimed at researchers and drug development professionals, it explores the foundational principles of VEGFR-2 inhibition, evaluates a spectrum of computational techniques from molecular docking to advanced deep learning, and addresses key challenges in model optimization. By presenting a comparative framework for validating predictions against experimental data, this review serves as a practical guide for selecting and applying computational tools to accelerate the discovery of novel, potent, and selective VEGFR-2 inhibitors with improved therapeutic profiles.

VEGFR-2 as a Therapeutic Target: Structural Basis for Inhibitor Binding

The Critical Role of VEGFR-2 in Tumor Angiogenesis and Cancer Progression

Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a transmembrane tyrosine kinase receptor that serves as the principal mediator of tumor angiogenesis, the process by which tumors develop new blood vessels to support their growth and metastasis. Expressed predominantly on vascular endothelial cells, VEGFR-2 controls critical cellular responses including proliferation, migration, survival, and vascular permeability upon binding to its primary ligand VEGF-A [1] [2]. The VEGF-VEGFR-2 signaling axis has become a cornerstone target for anticancer drug development because unlike physiological angiogenesis, which is tightly regulated, tumor-driven angiogenesis creates disorganized, leaky vessels that facilitate cancer progression and metastasis [3]. This review provides a comparative analysis of VEGFR-2 structure, function, and therapeutic targeting, with particular emphasis on binding affinity data and experimental methodologies that inform modern drug discovery approaches.

Structural Biology of VEGFR-2

Domain Architecture and Functional Regions

The molecular structure of VEGFR-2 is optimized for its role in angiogenesis signaling. Human VEGFR-2 is encoded by the kinase insert domain receptor (KDR) gene on chromosome 4q11-12 and consists of 1,356 amino acids forming a mature glycosylated protein of approximately 230 kDa [2]. The receptor comprises several critical domains, each with distinct functions:

• Extracellular Domain (20-764 aa): Contains seven immunoglobulin-like subdomains (IgD1-IgD7) that facilitate ligand binding and receptor dimerization [2].
• Transmembrane Domain (765-789 aa): An α-helical structure that anchors the receptor in the cell membrane and regulates kinase activity [2].
• Juxtamembrane Domain (790-833 aa): Controls the rate of receptor autophosphorylation [2].
• Tyrosine Kinase Domain (834-1162 aa): Includes ATP-binding, phosphotransferase, and kinase insert domains that catalyze phosphorylation reactions [2].
• Carboxy-Terminal Domain (1163-1356 aa): Contains key autophosphorylation sites that regulate endothelial cell proliferation and vascular permeability [2].

This sophisticated domain organization allows VEGFR-2 to transduce extracellular signals into intracellular responses with remarkable specificity, making it an ideal target for therapeutic intervention.

Structural Basis for Ligand Recognition and Activation

VEGFR-2 exhibits distinct binding preferences within the VEGF family, with VEGF-A recognized as its most potent activator [4] [5]. The extracellular Ig-like domains create a binding pocket that accommod specific VEGF isoforms, while the intracellular kinase domain undergoes conformational changes upon ligand binding, facilitating trans-autophosphorylation and initiation of downstream signaling cascades [2]. Recent structural analyses have revealed that VEGFR-2 can exist in both monomeric and dimeric forms on the plasma membrane even in the absence of ligand, suggesting complex regulation of its activation state [4].

Table 1: VEGFR-2 Structural Domains and Functional Characteristics

Domain	Amino Acid Residues	Key Structural Features	Functional Role
Extracellular Domain	20-764	7 immunoglobulin-like subdomains; 18 N-linked glycosylation sites	Ligand binding (VEGF-A, -C, -D); receptor dimerization
Transmembrane Domain	765-789	Single α-helical span	Membrane anchoring; regulates kinase activity
Juxtamembrane Domain	790-833	Regulatory α-helical structure	Controls autophosphorylation rate
Tyrosine Kinase Domain	834-1162	Two-lobed structure with hydrophobic pocket	Catalyzes phosphorylation reactions
Carboxy-Terminal Domain	1163-1356	Flexible tail with phosphorylation sites	Mediates cellular signaling and proliferation

VEGF/VEGFR-2 Signaling Pathway Mechanisms

The VEGF/VEGFR-2 signaling cascade begins when VEGF binding induces receptor dimerization and autophosphorylation of specific tyrosine residues within the intracellular domain. This phosphorylation activates multiple downstream pathways that collectively drive angiogenic processes [6] [2]:

• PI3K/Akt Pathway: Promotes endothelial cell survival and protection from apoptosis
• Ras/MAPK Pathway: Stimulates endothelial cell proliferation and differentiation
• FAK/paxillin Pathway: Regulates cell migration and cytoskeletal reorganization
• δ-Notch Signaling: Controls vascular patterning and branch formation

The critical importance of VEGFR-2 signaling is evidenced by the fact that VEGFR-2 deficiency results in abnormal vascular development and embryonic lethality, highlighting its non-redundant functions in angiogenesis [6].

VEGFR-2 Signaling Pathway Activation: This diagram illustrates the key molecular events following VEGF binding to VEGFR-2, leading to activation of multiple downstream pathways that collectively drive angiogenic processes.

Quantitative Analysis of VEGF-VEGFR2 Binding Affinities

Thermodynamic Framework for Binding Measurements

Understanding VEGF-VEGFR2 interactions requires sophisticated thermodynamic approaches that account for the receptor's association state. Recent research has demonstrated that VEGFR2 can exist as both monomers and dimers on the cell membrane, with significantly different binding affinities for VEGF [4]. A thermodynamic cycle accounting for all possible forms of VEGFR2 (monomers (M), dimers (D), ligand-bound monomers (LM), and ligand-bound dimers (LD)) has been developed to accurately describe these interactions [4].

The binding affinity of VEGF for monomeric VEGFR2 (KLM) and dimeric VEGFR2 (KLD) differs substantially, with measurements revealing an approximately 45-fold stronger binding to pre-formed dimers compared to monomers [4]. This distinction is critical for understanding receptor activation mechanisms and designing effective inhibitors.

Table 2: Experimentally Determined VEGF-VEGFR2 Binding Parameters

Binding Parameter	Symbol	Value	Units	Experimental Method
VEGF affinity for VEGFR2 monomers	KLM	9.6 × 10⁷ ± 1.8 × 10⁷	M⁻¹	Fully Quantified Spectral Imaging (FSI)
VEGF affinity for VEGFR2 dimers	KLD	4.3 × 10⁹ ± 0.5 × 10⁹	M⁻¹	Fully Quantified Spectral Imaging (FSI)
Ligand-free VEGFR2 dimerization	KR	(Receptors/μm²)⁻¹	(Receptors/μm²)⁻¹	Fluorescence Spectral Imaging
VEGF-bound monomer + monomer association	KLMD	(Receptors/μm²)⁻¹	(Receptors/μm²)⁻¹	Global fitting of thermodynamic model

Experimental Methodologies for Binding Affinity Determination

Fully Quantified Spectral Imaging (FSI)

The FSI methodology enables direct measurement of VEGFR2 surface density, bound VEGF density on individual cells, and free VEGF concentration in surrounding buffer [4]. This technique involves:

• Cell Preparation: Cells expressing fluorescently-labeled VEGFR2 are cultured under controlled conditions
• Ligand Binding: Alexa Fluor 594-labeled single-chain VEGF (scVEGF) is applied at varying concentrations
• Spectral Imaging: High-resolution imaging quantifies receptor and ligand densities on cell surfaces
• Global Fitting: Data from multiple cells at different VEGF concentrations are fit to thermodynamic models

This approach overcomes limitations of traditional binding assays by accounting for receptor density effects on dimer formation and activation states [4].

Surface Plasmon Resonance (SPR)

SPR provides label-free kinetic analysis of molecular interactions by measuring changes in refractive index at a sensor surface [7]. Standard protocols include:

• Surface Functionalization: Immobilization of VEGFR2 extracellular domain on sensor chips
• Ligand Injection: VEGF isoforms flowed over the surface at varying concentrations
• Kinetic Measurement: Real-time monitoring of association and dissociation phases
• Data Analysis: Fitting sensorgrams to 1:1 Langmuir binding models to determine kinetic constants

SPR validation typically employs a χ²-to-Rmax ratio heuristic, where values ≤1.0 indicate true 1:1 Langmuir interactions, while higher values suggest non-specific binding [7].

Comparative Analysis of VEGFR-2 Inhibitors

Small Molecule Inhibitors: Efficacy and Binding Characteristics

Small molecule inhibitors targeting VEGFR2 have emerged as valuable therapeutic options, with diverse chemical scaffolds showing efficacy across multiple cancer types. These compounds typically function as ATP-competitive inhibitors that prevent receptor autophosphorylation and downstream signaling [8] [5].

Recent developments include nicotinamide-thiadiazol hybrids such as compound 7a, which demonstrates potent VEGFR-2 inhibitory activity (IC₅₀ = 0.095 ± 0.05 μM) and strong anticancer effects against breast cancer cell lines (IC₅₀ = 4.64 ± 0.3 μM in MDA-MB-231 and 7.09 ± 0.5 μM in MCF-7) [8]. Computational studies including molecular docking and 200 ns molecular dynamics simulations confirm stable interactions between these inhibitors and the VEGFR2 ATP-binding pocket [8].

Table 3: Experimentally Determined Efficacy of Representative VEGFR-2 Inhibitors

Compound	VEGFR-2 IC₅₀ (μM)	Cellular IC₅₀ (μM)	Cancer Models	Key Mechanisms
Nicotinamide-thiadiazol hybrid 7a	0.095 ± 0.05	4.64 (MDA-MB-231) 7.09 (MCF-7)	Breast cancer	S-phase cell cycle arrest; Caspase-3 activation (8.2-fold)
Sorafenib (reference)	Comparable efficacy	Comparable to 7a	Multiple cancer types	Multi-kinase inhibition
Axitinib (reference)	-	-	Renal cell carcinoma	Selective VEGFR inhibition
Pazopanib (reference)	-	-	Soft tissue sarcoma	Multi-targeted receptor inhibition

Emerging Computational Approaches for Inhibitor Discovery

Advanced computational methods are revolutionizing VEGFR2 inhibitor development:

• Deep Learning Models: Geometric-enhanced molecular representation learning (GEM) employing graph neural networks (GNN) predicts compound activity and identifies candidates with improved properties over existing drugs like axitinib [9]
• Molecular Dynamics Simulations: 200 ns simulations validate stable binding interactions and residence times for promising inhibitors [8] [10]
• Pharmacophore Modeling: Identifies essential structural features for VEGFR2 binding, including heteroaromatic groups for hinge region interactions and hydrophobic tails for allosteric pocket binding [8]

These in silico approaches enable rapid screening of compound libraries and optimization of drug candidates before costly synthetic and experimental procedures [9] [10].

Experimental Protocols for VEGFR-2 Research

Molecular Docking Protocol for VEGFR-2 Inhibitor Screening

Standardized molecular docking procedures facilitate comparative assessment of VEGFR2 inhibitor binding:

• Protein Preparation: Retrieve VEGFR2 crystal structure (e.g., PDB ID: 1Y6A); remove co-crystallized ligands and water molecules; add hydrogen atoms; assign partial charges [10]
• Ligand Preparation: Obtain 3D structures from databases (PubChem, ZINC); optimize geometry using molecular mechanics; generate possible tautomers and protonation states [10]
• Docking Parameters: Use MolDock or similar scoring functions; set grid resolution to 0.3Å; population size 50; maximum iterations 1,500 [10]
• Validation: Redock known inhibitors to validate protocol accuracy; compare binding poses with experimental structures [10]

VEGFR-2 Inhibition Assay Protocol

Cell-free kinase assays quantify direct inhibitory activity:

• Reagent Preparation: Purified VEGFR2 kinase domain; appropriate peptide substrate; ATP solution; detection reagents [8]
• Reaction Setup: Incubate inhibitor with enzyme in reaction buffer; add ATP/substrate mixture to initiate reaction; terminate after specific duration [8]
• Detection Methods: Use ELISA-based phosphorylation detection or luminescence-based ATP consumption assays [8]
• Data Analysis: Calculate percentage inhibition relative to controls; determine IC₅₀ values using nonlinear regression [8]

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key Research Reagents for VEGFR-2 Investigation

Reagent / Method	Specific Example	Research Application	Key Features
scVEGF fluorescent probes	Alexa Fluor 594-labeled scVEGF	Binding affinity measurements (FSI)	Site-specific fluorophore labeling; maintained bioactivity [4]
SPR kits & reagents	CM5 sensor chips; amine coupling kits	Kinetic binding analysis	Label-free interaction kinetics; high sensitivity [7]
VEGFR2 structural templates	PDB IDs: 1Y6A, 3HNG, 4BSJ	Computational docking studies	High-resolution crystal structures [10]
Kinase assay systems	ADP-Glo Kinase Assay	Inhibitor screening	Luminescence-based detection; high throughput [8]
Deep learning platforms	RD-Kit; GEM-GNN models	de novo inhibitor design	Generates novel compounds with optimized properties [9] [10]

VEGFR-2 remains a critical target for anti-angiogenic cancer therapy, with ongoing research refining our understanding of its structure-function relationships and activation mechanisms. The quantitative binding data and experimental methodologies reviewed here provide frameworks for comparing inhibitor efficacy and developing improved therapeutic strategies. Future directions include leveraging deep learning for multi-target inhibitor design, developing resistance-breaking compounds, and creating personalized approaches based on individual patient VEGF-VEGFR2 signaling profiles. As structural biology and computational methods continue to advance, the precision and efficacy of VEGFR2-targeted therapies will undoubtedly improve, offering new hope for cancer patients with angiogenesis-dependent malignancies.

Key Structural Domains and Pharmacophore Features of the VEGFR-2 Binding Site

Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a transmembrane tyrosine kinase receptor that serves as the primary mediator of physiological and pathological angiogenesis. Expressed predominantly on vascular endothelial cells, VEGFR-2 binding to its ligands (primarily VEGF-A, VEGF-C, and VEGF-D) initiates a signaling cascade that promotes endothelial cell proliferation, migration, survival, and vascular permeability [2] [11]. In pathological conditions, particularly cancer, tumor growth and metastasis are highly dependent on angiogenesis, making VEGFR-2 a critical therapeutic target for anti-cancer drug development [12] [13]. The structural characterization of VEGFR-2's binding site and the definition of its essential pharmacophore features provide the foundation for rational drug design aimed at developing effective VEGFR-2 inhibitors with improved affinity and selectivity profiles.

Structural Architecture of VEGFR-2

The full-length human VEGFR-2 is a 1356-amino acid transmembrane glycoprotein with a molecular weight of approximately 230 kDa in its mature, glycosylated form [2] [11]. Its domain architecture consists of multiple structural components, each with distinct functional roles in ligand binding, receptor dimerization, and signal transduction, as detailed in Table 1.

Table 1: Structural Domains of VEGFR-2 and Their Functional Roles

Domain	Amino Acid Residues	Key Structural Features	Functional Role
Signal Peptide	1-19	N-terminal peptide	Guides receptor to membrane
Extracellular Domain (ECD)	20-764	7 Ig-like domains (D1-D7)	Ligand binding and receptor dimerization
Transmembrane Domain (TMD)	765-789	α-helical structure	Membrane anchoring and orientation
Juxtamembrane Domain (JMD)	790-833	Single α-helix	Regulates autophosphorylation rate
Tyrosine Kinase Domain (TKD)	834-1162	Two-lobed structure with hydrophobic pocket	Catalytic activity and ATP binding
Carboxy-Terminal Domain	1163-1356	Flexible tail	Autophosphorylation sites for signaling

The Ligand-Binding Extracellular Domain

The extracellular domain of VEGFR-2 comprises seven immunoglobulin-like (Ig-like) homology domains (D1-D7). Structural studies have revealed that the high-affinity ligand-binding site is primarily composed of domains D2 and D3 (referred to as VEGFR-2D23) [14] [15]. Crystal structures of VEGF ligands in complex with VEGFR-2D23 show a symmetrical 2:2 complex stoichiometry, where left-handed twisted receptor domains wrap around the 2-fold axis of the VEGF dimer [14]. The D2 domain is globular with relatively short β-strands, while D3 is more elongated with longer β-strands, and they are connected by a short linker peptide that contributes to binding interactions [14].

The binding interface between VEGF and VEGFR-2 involves specific structural elements of the ligand. In VEGF-C, for instance, an N-terminal α-helix and three peptide loops (L1-L3) interact with the receptor domains [14]. Two of these loops bind to both D2 and D3 domains, while one interacts primarily with D3. The groove separating the two VEGF-C monomers accommodates the D2/D3 linker region [14]. This detailed structural understanding provides critical insights for designing inhibitors that target specific regions of the ligand-binding site.

Diagram 1: VEGFR-2 Domain Architecture and Ligand Binding

Pharmacophore Features of the VEGFR-2 Binding Site

Key Structural Determinants of Ligand Binding

The VEGFR-2 binding site contains specific pharmacophore features that determine ligand specificity and binding affinity. Structural analyses comparing different VEGF family members (VEGF-A, VEGF-C, and VEGF-E) bound to VEGFR-2 have revealed that receptor specificity is determined by an N-terminal α-helix and three peptide loops in the VEGF structure [14]. These elements interact with complementary surfaces on the VEGFR-2 D2 and D3 domains, with variation in D2-D3 twist angles observed between different ligand-receptor complexes [15].

Biochemical and structural studies have identified critical VEGFR-2 residues essential for binding both VEGF-A and VEGF-C [14]. The interaction is characterized by a combination of van der Waals contacts, ionic interactions, and hydrogen bonding networks. The binding affinity is enthalpically and entropically favorable, with the presence of the D3 domain being essential for high-affinity binding, although the D2 domain alone is sufficient for basal binding activity [14].

ATP-Binding Site in the Tyrosine Kinase Domain

For small molecule inhibitors targeting the intracellular region, the ATP-binding site within the tyrosine kinase domain represents the primary pharmacophore target. This domain features a characteristic two-lobed structure that forms an active center between them [2]. Key pharmacophore elements include:

• A hydrophobic pocket at the intracellular N-terminus containing a glycine-rich motif (GXGXXG, residues 841-846) and ATP-phosphate binding loop [2]
• The activation loop (A-loop, residues 1045-1075) and catalytic loop (HRDLAARN, residues 1026-1033) that perform catalytic functions [11]
• Critical phosphorylation sites (Tyr801, Tyr951, Tyr1175, and Tyr1214) that mediate downstream signaling [11]

Small molecule VEGFR-2 inhibitors are classified into two types based on their binding mode: type 1 (DFG-in) inhibitors that target the active kinase conformation, and type 2 (DFG-out) inhibitors that bind to an allosteric hydrophobic pocket (HYD-II) in addition to the hinge region, potentially offering improved selectivity [12].

Experimental Methodologies for VEGFR-2 Binding Site Characterization

Structural Biology Approaches

The structural characterization of VEGFR-2 and its binding sites has relied on multiple experimental techniques, each providing complementary information, as summarized in Table 2.

Table 2: Key Experimental Methods for VEGFR-2 Structural Characterization

Method	Experimental Setup	Key Output	Applications in VEGFR-2 Research
X-ray Crystallography	Crystal structure of VEGF/VEGFR-2D23 complexes at 2.7-3.2 Å resolution	Atomic-level coordinates of binding interfaces	Mapping ligand-receptor interactions and binding stoichiometry [14] [16]
Isothermal Titration Calorimetry (ITC)	Titration of VEGF ligands into VEGFR-2D23 or full ECD at constant temperature	Binding affinity (Kd), stoichiometry (n), enthalpy (ΔH), entropy (ΔS)	Thermodynamic profiling of ligand binding [14] [15]
Small-Angle X-ray Scattering (SAXS)	Scattering analysis of VEGF-E/VEGFR-2 ECD complex in solution	Low-resolution structural model and oligomeric state	Identification of homotypic interactions in D4-7 domains [15]
Multi-Angle Laser Light Scattering (MALS)	Size-exclusion chromatography coupled with light scattering	Molecular weight and complex stoichiometry	Confirmation of 2:2 ligand:receptor stoichiometry [14]

Computational and Virtual Screening Methods

Computational approaches have become indispensable for identifying and optimizing VEGFR-2 inhibitors, employing sophisticated workflows that integrate multiple methodologies, as illustrated in Diagram 2.

Diagram 2: Computational Workflow for VEGFR-2 Inhibitor Identification

The specific protocols for key computational methods include:

Pharmacophore Modeling and Screening:

• Generation of structure-based pharmacophores using receptor-ligand complexes from PDB [17]
• Consideration of six standard pharmacophore features: hydrogen bond acceptor, hydrogen bond donor, positive ionizable center, negative ionizable center, hydrophobic center, and ring aromatic center [17]
• Validation using enrichment factors (EF) and receiver operating characteristic (ROC) curves, with models considered reliable if AUC > 0.7 and EF > 2 [17]

Molecular Docking Protocols:

• Protein preparation including removal of water molecules, addition of hydrogen atoms, and correction of missing residues [18]
• Grid box placement centered on the binding site with typical size of 20Å × 20Å × 20Å [18]
• Use of exhaustiveness parameter set to 100 for thorough search space exploration [18]
• Scoring based on weighted sum of van der Waals interactions, hydrogen bonding, hydrophobic free energy, and desolvation effects [12]

Molecular Dynamics Simulations:

• System setup with solvation in TIP3P water model and neutralization with ions [17] [12]
• Production simulations typically running for 100 ns using AMBER or similar software [17] [12]
• Binding free energy calculations using MM/PBSA or MM/GBSA methods on snapshots from the trajectory [17] [18] [12]

Research Reagent Solutions for VEGFR-2 Studies

Table 3: Essential Research Reagents for VEGFR-2 Binding Studies

Reagent / Resource	Specifications	Research Application	Key Function
VEGFR-2 Protein Constructs	VEGFR-2D23 (domains 2-3) or full ECD; expressed in insect or mammalian cells	Structural studies and binding assays	Provides binding interface for ligand interaction studies [14]
VEGF Ligands	VEGF-A, VEGF-C, VEGF-E; wild-type and mutant forms	Functional assays and complex formation	Activation of VEGFR-2 for signaling studies [14] [15]
Reference Inhibitors	Tivozanib, Sorafenib, Axitinib, Regorafenib	Control compounds for benchmarking	Positive controls for binding and inhibition assays [12] [13]
Crystallization Reagents	Commercial screening kits and optimization solutions	X-ray crystallography of complexes	Production of diffraction-quality crystals [14] [16]
Virtual Screening Databases	ZINC20, ChemDiv, ChEMBL, PubChem	Compound sourcing for inhibitor discovery	Libraries for computational screening [17] [12] [13]

The comprehensive structural description of VEGFR-2's key binding domains and their pharmacophore features provides an essential foundation for targeted drug discovery efforts. The precise mapping of the ligand-binding site in extracellular domains D2 and D3, coupled with detailed characterization of the intracellular ATP-binding pocket, enables rational design of inhibitors with optimized binding affinity and specificity. Integrated methodological approaches combining high-resolution structural biology, thermodynamic binding measurements, and advanced computational screening continue to drive the discovery of novel VEGFR-2 inhibitors with potential therapeutic applications in cancer and other angiogenesis-dependent diseases. As structural insights deepen and computational methods evolve, the prospect of developing increasingly selective and effective VEGFR-2-targeted therapies continues to strengthen, highlighting the critical importance of fundamental structural and pharmacophore research in advancing this therapeutic field.

Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a primary mediator of tumor angiogenesis, the process by which new blood vessels form to supply nutrients and oxygen to growing cancers [19] [20]. The binding of VEGF to VEGFR-2 triggers receptor dimerization and autophosphorylation, activating downstream signaling pathways including PI3K-AKT-mTOR, Cdc42-p38-MAPK, and RAS-RAF-MEK-ERK1/2, which collectively promote endothelial cell proliferation, survival, migration, and vascular permeability [21] [6]. Given its critical role in cancer progression, VEGFR-2 has emerged as a prominent therapeutic target, leading to the development of diverse small-molecule inhibitors classified according to their binding modes and interaction with the kinase's conformational states [21] [12]. This guide provides a comprehensive comparison of Type I, II, and III VEGFR-2 inhibitors, detailing their structural basis, experimental characterization, and relevance to drug discovery.

Structural Basis and Classification of VEGFR-2 Inhibitors

VEGFR-2 inhibitors are categorized into three main types based on their binding mode to the kinase domain and their effect on its conformational state [21] [20] [12]. The table below summarizes the core characteristics of each inhibitor type.

Table 1: Classification and Characteristics of VEGFR-2 Inhibitors

Inhibitor Type	Binding Site	Kinase Conformation	Key Interactions	Representative Inhibitors
Type I	ATP-binding pocket	Active (DFG-in)	Hydrogen bond with Cys919 backbone in hinge region [20]	Axitinib, Pazopanib, Sunitinib, Vandetanib [21]
Type II	ATP pocket + adjacent hydrophobic pocket	Inactive (DFG-out)	H-bond with hinge region (Cys919); H-bond donor/acceptor pair with Glu885 and Asp1046; hydrophobic interactions in allosteric pocket [20] [12]	Sorafenib, Lenvatinib, Apatinib (Rivoceranib), Tivozanib [21] [12]
Type III	Allosteric site, remote from ATP pocket	Inactive	Binds irreversibly outside the ATP-binding pocket [21]	(Developmental stage, no approved drugs listed)

The following diagram illustrates the binding modes of these inhibitor types within the VEGFR-2 kinase domain.

Experimental Characterization of Inhibitor Binding

Biochemical Kinase Activity Assays

Protocol Summary: The potency of VEGFR-2 inhibitors is typically determined by measuring their half-maximal inhibitory concentration (IC₅₀) against the purified VEGFR-2 kinase domain in biochemical assays [19]. A common method involves screening compounds against a panel of up to 270 human kinases to assess selectivity, using a single concentration (e.g., 10 µM) in initial screens followed by dose-response curves for hit validation [19]. For instance, one study evaluated rivoceranib against 10 FDA-approved kinase inhibitors, reporting a VEGFR-2 IC₅₀ of 16 nM and demonstrating superior selectivity compared to multi-targeted reference inhibitors [19].

Key Findings:

• Rivoceranib (a Type II inhibitor) showed high potency (IC₅₀ = 16 nM) and greater selectivity for VEGFR-2 over other kinases, suggesting a potentially improved toxicity profile [19].
• Thieno[2,3-d]pyrimidine-based derivatives (Type II) exhibited IC₅₀ values as low as 21 nM in VEGFR-2 kinase inhibition assays [20].
• Furo[2,3-d]pyrimidine-based derivatives (Type II) demonstrated up to 99.5% inhibition of human umbilical vein endothelial cell (HUVEC) proliferation at 10 µM concentration, confirming functional anti-angiogenic activity [20].

Structure-Based Virtual Screening

Protocol Summary: Virtual screening employs computational methods to identify novel VEGFR-2 inhibitors from large chemical libraries [22] [12]. A standard workflow includes:

• Library Preparation: Downloading and filtering (e.g., using PAINS filter) purchasable compound databases (e.g., ZINC20, NCI database) for drug-like properties [21] [12].
• Ligand-Based Screening: Using tools like ROCS (Rapid Overlay of Chemical Structures) to find compounds with shape similarity to a known reference inhibitor (e.g., tivozanib) [12].
• Molecular Docking: Docking shortlisted compounds into the VEGFR-2 binding site (e.g., using PDB ID: 4ASD) with software such as ICM-PRO or AutoDock Vina to predict binding poses and affinities [22] [12].
• ADMET Filtering: Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties using tools like SwissADME or QikProp to prioritize candidates with favorable pharmacokinetic profiles [21] [22].

Key Findings: This approach has identified novel chemotypes from natural product libraries [22] and synthetic databases [21] [12], successfully pinpointing compounds with predicted high affinity for VEGFR-2 (e.g., binding energies from -11.0 to -11.5 kcal/mol) [22].

Molecular Dynamics (MD) Simulations and Binding Free Energy Calculations

Protocol Summary: MD simulations assess the stability of protein-ligand complexes and calculate binding free energies using methods like MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) or MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) [21] [22] [12]. A typical protocol involves:

• System Setup: Solvating the docked VEGFR-2-inhibitor complex in a water box (e.g., TIP3P model) and adding ions to physiological concentration (150 mM NaCl) [12].
• Simulation Run: Performing 100-200 ns of conventional MD simulation after minimization and equilibration steps, using AMBER or similar software with the ff14SB force field for the protein and GAFF for the ligand [22] [12].
• Trajectory Analysis: Calculating the root-mean-square deviation (RMSD) and binding free energy by collecting hundreds of snapshots from the stable simulation trajectory [22] [12].

Key Findings: Studies have identified stable binding for candidate inhibitors, such as compound 737734 from the NCI database, which showed high stability in complex with both VEGFR-2 and K-RAS G12C in 100 ns simulations [21]. Another study found that a novel benzoxazole derivative (35d) demonstrated a better propensity for stabilizing VEGFR-2 than the control drug sorafenib [23].

Table 2: Experimental Data for Representative VEGFR-2 Inhibitors

Inhibitor (Type)	Reported IC₅₀ / Potency	Key Experimental Findings	Selectivity Profile
Rivoceranib (Type II)	16 nM [19]	Binds intracellular ATP-binding domain; blocks VEGF-mediated signaling, endothelial cell proliferation, and tumor angiogenesis [19]	High selectivity for VEGFR-2 in a 270-kinase panel [19]
Thieno[2,3-d]pyrimidine 21e (Type II)	21 nM [20]	Orally administered at 10 mg/kg/day for 8 days demonstrated potent anticancer activity in a murine EAC model; reduced microvessel density and VEGFR-2 phosphorylation [20]	Data not specified for broad kinase panel
Furo[2,3-d]pyrimidine 15b (Type II)	99.5% HUVEC inhibition at 10 µM [20]	Showed the strongest anti-proliferative activity against HUVECs in vitro [20]	Data not specified for broad kinase panel
Sorafenib (Type II)	90 nM [20]	Approved for metastatic renal cell carcinoma and unresectable hepatocellular carcinoma [20]	Targets RAF-1, VEGFR1-3, PDGFR-β, FLT-3, c-KIT [19]

The Scientist's Toolkit: Key Research Reagents and Solutions

Table 3: Essential Research Materials for VEGFR-2 Inhibitor Studies

Reagent / Resource	Function and Application	Example Sources / References
Recombinant VEGFR-2 Kinase Domain	In vitro biochemical assays to determine inhibitor IC₅₀ values.	Carna Biosciences (Cat. No. 08-491-20N) [19]
Human Umbilical Vein Endothelial Cells (HUVECs)	Cellular models to study anti-proliferative and anti-angiogenic effects of inhibitors.	Primary cell cultures [20]
Kinase Profiling Panels	Comprehensive selectivity screening against hundreds of human kinases to identify off-target effects.	scanMAX kinase panel [24]; 270-kinase panel [19]
VEGFR-2 Crystal Structures	Structural basis for docking and structure-based drug design (e.g., PDB IDs: 4ASD, 4ASE, 4AGC).	Protein Data Bank (PDB) [22] [12]
Compound Libraries	Sources for virtual screening and experimental high-throughput screening (HTS).	ZINC20, NCI database, African Natural Products Database [21] [22] [12]

Clinical and Research Implications

The classification of VEGFR-2 inhibitors has direct implications for drug design and therapeutic outcomes. Type II inhibitors are often pursued for their potential improved kinase selectivity and slower off-rates, which can translate to enhanced efficacy and reduced side effects [20]. For example, the high selectivity of rivoceranib for VEGFR-2 is hypothesized to address clinical limitations associated with the off-target effects of multi-kinase inhibitors [19]. Furthermore, the strategic inhibition of both VEGFR-2 and mutant K-RAS G12C represents a promising dual-targeting approach to overcome resistance to anti-angiogenic monotherapies [21]. The VEGFR-2 inhibitors market is gaining significant traction, with ongoing clinical trials evaluating next-generation inhibitors both as monotherapies and in combination with immune checkpoint inhibitors across multiple tumor types [25].

Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) is the principal mediator of VEGF-induced angiogenic signaling and a well-validated target for anti-cancer therapy [26]. As the main signaling receptor for VEGF, VEGFR2 activation stimulates downstream pathways including PLC-γ-Raf kinase-MEK-MAPK and PI3K-AKT, promoting endothelial cell proliferation, migration, survival, and new blood vessel formation [27]. Tumor angiogenesis enables the supply of oxygen and nutrients to the tumor microenvironment, facilitating tumor growth, metastasis, and drug resistance across numerous cancer types [19]. Despite the clinical approval of multiple VEGFR2-targeted agents, their therapeutic efficacy remains constrained by two principal challenges: the development of inherent and acquired resistance mechanisms, and the manifestation of treatment-related toxicities often linked to off-target effects [19] [26]. This guide provides a comparative analysis of approved VEGFR2 inhibitors, focusing on experimental data regarding their potency, selectivity, and clinical performance, thereby contextualizing them within the broader research framework of comparative binding affinity prediction for VEGFR2 inhibitor development.

Comparative Biochemical Profiling of VEGFR2 Inhibitors

Quantitative Analysis of Inhibitor Potency and Selectivity

Understanding the biochemical profiles of VEGFR2 inhibitors is crucial for rational therapy selection. Table 1 summarizes key experimental data for a panel of inhibitors, highlighting their VEGFR2 inhibition potency (IC50) and selectivity profiles.

Table 1: Biochemical and Selectivity Profiles of VEGFR2 Inhibitors

Inhibitor	VEGFR2 IC50 (nM)	Primary Targets	Selectivity Profile	Key Off-Target Kinases
Rivoceranib	16 [19]	VEGFR2, c-KIT, c-SRC [19]	High selectivity for VEGFR2 [19]	Minimal off-target activity in 270-kinase panel [19]
Axitinib	Information Missing	VEGFR1-3 [19]	Information Missing	Information Missing
Cabozantinib	Information Missing	MET, RET, AXL, VEGFR2, FLT3, c-KIT [19]	Information Missing	Information Missing
Lenvatinib	Information Missing	VEGFR1-3, PDGFRα, FGFR, KIT, RET [19]	Information Missing	Information Missing
Regorafenib	Information Missing	VEGFR1-3, TIE2, KIT, RET, RAF1, BRAF, PDGFR, FGFR [19]	Information Missing	Information Missing
Sorafenib	Information Missing	RAF-1, VEGR1-3, PDGFR-β, FLT-3, c-KIT [19]	Information Missing	Information Missing
Sunitinib	Information Missing	VEGFR1-3, PDGFR, c-KIT, FLT-3, CSF1R, RET [19]	Information Missing	Information Missing
Tivozanib	Information Missing	VEGFR1-3, PDGFRα/β, c-KIT [19]	Information Missing	Information Missing
Vandetanib	Information Missing	VEGFR-2, EGFR, RET [19]	Information Missing	Information Missing
AZD4547	24 [28]	FGFR1/2/3, VEGFR2 [28]	Potent FGFR1-3 inhibitor [28]	FGFR1 (0.2 nM), FGFR2 (1.8 nM), FGFR3 (2.5 nM) [28]

A head-to-head biochemical analysis of rivoceranib and 10 FDA-approved tyrosine kinase inhibitors with known VEGFR2 activity demonstrated that while rivoceranib's potency (IC50 = 16 nM) was within the observed range of reference inhibitors, it displayed superior selectivity for VEGFR2 across a panel of 270 kinases [19]. This enhanced selectivity is clinically significant, as toxicities associated with available VEGFR2 inhibitors are thought to be partly due to their effects against kinases other than VEGFR2 [19].

Experimental Protocols for Binding and Kinase Activity Assessment

Surface Plasmon Resonance (SPR) for Binding Affinity Determination: The affinity of inhibitors for VEGFR2 can be quantified using surface plasmon resonance. The experimental workflow involves immobilizing a biotinylated recombinant cytoplasmic domain of VEGFR2 (e.g., amino acid residue 790 to 1356) on a streptavidin-coated chip [19]. Test compounds are dissolved in DMSO and diluted in assay buffer (e.g., 50 mM Tris pH 7.5, 0.05% Tween 20, 150 mM NaCl, 5 mM MgCl2) [19]. Serial dilutions of the inhibitor are flowed over the chip, and binding kinetics are measured. For rivoceranib, this method has demonstrated strong binding to the VEGFR2 extracellular domain [19].

Biochemical Kinase Inhibition Profiling: Kinase inhibition potency (IC50) is determined using kinase activity assays. Recombinant VEGFR2 kinase domain is incubated with test compounds across a concentration range (typically from nanomolar to micromolar). The reaction is initiated by adding ATP and a specific substrate, and kinase activity is measured via ADP production or phosphosubstrate detection [19]. Residual kinase activity is plotted against inhibitor concentration to calculate IC50 values. For selectivity profiling, this assay is replicated against a broad panel of human kinases (e.g., 270 kinases) to identify off-target interactions [19].

Resistance Mechanisms and Toxicity Profiles

Molecular Pathways of Resistance to VEGFR2 Inhibition

Acquired resistance to VEGFR2 inhibitors involves adaptive cellular responses, including upregulation of alternative pro-angiogenic pathways. A key mechanism identified in KRAS G12D inhibitor-resistant models involves PI3Kγ activation, leading to an autocrine VEGFA-VEGFR2 signaling loop and epithelial-to-mesenchymal transition (EMT) induction [29]. Single-cell RNA sequencing of resistant patient-derived organoids revealed enrichment of angiogenesis, hypoxia, and EMT signatures, with uniformly elevated VEGFA expression and VEGFR2 phosphorylation across all resistant models [29]. This redundancy in pro-angiogenic signaling highlights the potential limitation of selective VEGFR2 inhibition and provides a rationale for combination therapies targeting parallel pathways.

Figure 1: VEGFR2-Mediated Resistance Pathway. This diagram illustrates the mechanism of acquired resistance to KRAS G12D inhibition driven by PI3Kγ activation, leading to enhanced VEGFA-VEGFR2 signaling and subsequent angiogenesis and EMT.

Comparative Clinical Efficacy and Safety in Metastatic Colorectal Cancer

Real-world evidence provides direct comparisons of the clinical performance of different VEGFR2 inhibitors. Table 2 summarizes efficacy and safety outcomes for fruquintinib and regorafenib in metastatic colorectal cancer (mCRC) based on a retrospective study of 105 patients.

Table 2: Comparison of Fruquintinib vs. Regorafenib in Metastatic Colorectal Cancer [30]

Parameter	Fruquintinib (n=55)	Regorafenib (n=50)	P-value
Objective Response Rate (ORR)	6.1%	2.0%	Not significant
Disease Control Rate (DCR)	65.3%	54.2%	Not significant
Median Overall Survival (mOS)	14.2 months	12.0 months	0.057
Median Progression-Free Survival (mPFS)	4.4 months	3.5 months	0.150
Common Adverse Events	Grade 3 hypertension [30]	Hand-foot syndrome [30]	N/A

While no significant difference was observed in median OS and PFS between the two drugs, their toxicity profiles differed. Regorafenib was associated with a higher incidence of hand-foot syndrome, while fruquintinib was more prone to causing grade 3 hypertension [30]. The sequence of administration also impacted outcomes, with patients receiving regorafenib followed by fruquintinib experiencing significantly longer OS (15.0 months) compared to the reverse sequence (8.3 months, p=0.019) [30].

Emerging Strategies to Overcome Clinical Challenges

Combination Therapies to Counter Resistance

Combining VEGFR2 inhibitors with other therapeutic modalities has shown promise in overcoming resistance. The combination of fruquintinib or regorafenib with anti-PD-1 immunotherapy demonstrated synergistic effects [30]. For fruquintinib combined with anti-PD-1 therapy, median PFS and OS were significantly longer than with fruquintinib monotherapy (mPFS: 5.9 vs 3.0 months, p=0.009; mOS: 17.5 vs 11.3 months, p=0.008) [30]. Similarly, the combination of VEGFR2 blockade with KRAS G12D inhibition more effectively reduced tumor growth, angiogenesis, and proliferation markers in resistant xenograft models than either monotherapy alone [29].

Novel Inhibitor Development and Screening Technologies

Advanced computational approaches are being employed to develop next-generation VEGFR2 inhibitors with improved efficacy and reduced toxicity. Three-dimensional physiologically-informed deep learning models, such as geometric-enhanced molecular representation learning (GEM) employing graph neural networks (GNN), have identified candidate compounds with potentially more favorable properties than existing drugs like axitinib [9]. These methods utilize structural modeling and flexible docking to screen for high-affinity inhibitors and explore their mechanism of action, enabling the identification of small-molecule compounds with consistently improved properties [9].

Figure 2: AI-Driven Drug Discovery Workflow. This diagram outlines the integrated computational approach for novel VEGFR2 inhibitor identification, combining deep learning prediction with structural modeling for improved candidate selection.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagents for VEGFR2 Inhibitor Studies

Reagent/Material	Function/Application	Example Specifications
Recombinant VEGFR2 Kinase Domain	Biochemical kinase activity assays; Binding studies	Human, cytoplasmic domain (e.g., residues 790-1356) [19]
Kinase Profiling Panel	Selectivity screening; Off-target identification	Broad kinase panels (e.g., 270 human kinases) [19]
SPR Biosensor Chips	Binding affinity and kinetics measurement	Streptavidin-coated chips for biotinylated protein immobilization [19]
Phospho-Specific Antibodies	Detection of VEGFR2 phosphorylation and downstream signaling	Anti-pVEGFR2 (Tyr1175), anti-pERK, anti-pAKT [29]
Patient-Derived Organoids (PDOs)	Resistance mechanism studies; Preclinical validation	KRAS mutant PDOs for resistance modeling [29]
Graph Neural Network Platforms	Predictive modeling of compound activity	GEM-GNN models for molecular representation learning [9]

The clinical challenges of resistance and toxicity associated with approved VEGFR2 inhibitors underscore the need for continued refinement of therapeutic strategies. Biochemical analyses reveal significant differences in selectivity profiles among inhibitors, with rivoceranib demonstrating high selectivity for VEGFR2 compared to multi-targeted inhibitors such as regorafenib, lenvatinib, and cabozantinib [19]. These selectivity differences correlate with distinct toxicity patterns observed in clinical practice, where multi-kinase inhibitors exhibit broader adverse event profiles [19] [30]. Emerging resistance mechanisms, particularly those involving adaptive upregulation of VEGFA-VEGFR2 signaling and activation of alternative pathways such as PI3Kγ, highlight the potential of combination therapies that simultaneously target VEGFR2 and complementary pathways [30] [29]. The integration of advanced computational methods, including deep learning and structural modeling, promises to accelerate the development of next-generation VEGFR2 inhibitors with optimized binding affinity, enhanced selectivity, and improved therapeutic indices [9].

Computational Arsenal: From Docking to Deep Learning for Affinity Prediction

Molecular docking and dynamics (MD) simulations have become indispensable tools in structural biology and computer-aided drug design, providing critical insights into ligand-receptor interactions, binding affinities, and conformational stability. Within the context of comparative binding affinity prediction for Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) inhibitors, these computational techniques enable researchers to evaluate potential drug candidates in silico before costly synthetic and experimental procedures. VEGFR-2, a transmembrane receptor tyrosine kinase, serves as a pivotal therapeutic target due to its central role in tumor angiogenesis across various carcinomas, including breast, renal, and papillary thyroid cancers [31] [32] [33]. The receptor's activation triggers downstream signaling pathways such as PI3K/Akt and MAPK/ERK, promoting endothelial cell survival, proliferation, and migration [32] [6]. This review objectively compares the performance of different computational methodologies employed in recent VEGFR-2 inhibitor studies, supported by experimental data that validate the predictive capabilities of these in silico approaches.

VEGFR-2 as a Therapeutic Target: Biological Context

VEGFR-2 is a 151-kDa member of the receptor tyrosine kinase family, characterized by an extracellular domain composed of seven immunoglobulin-like domains, a single transmembrane domain, and an intracellular kinase domain [4] [6]. The mature, fully glycosylated 230 kD form of the receptor initiates intracellular signal transduction when activated by its primary ligand, VEGF-A [32]. Upon VEGF binding, VEGFR-2 undergoes dimerization and autophosphorylation of key tyrosine residues (Y1054 and Y1059), activating downstream signaling cascades including PI3K/Akt, Ras/MAPK, and Src pathways that ultimately promote angiogenesis [6] [34].

In pathological conditions, particularly in various cancer types, VEGFR-2 is overexpressed, making it a prime target for selective cancer therapies [32]. Inhibition strategies focus on blocking VEGFR-2 activation to prevent tumor angiogenesis without affecting healthy tissues. The structural domains of VEGFR-2 and its key signaling pathways are illustrated below.

Figure 1: VEGFR-2 Signaling Pathway and Activation Mechanism. VEGF binding to the extracellular domain triggers conformational changes through the transmembrane domain, leading to kinase domain phosphorylation and activation of downstream pathways that promote angiogenesis.

Comparative Analysis of Computational Methodologies

Molecular Docking Approaches and Performance

Molecular docking serves as the foundational computational method for predicting binding poses and initial affinity estimates between VEGFR-2 and potential inhibitors. Recent studies have employed diverse docking methodologies with varying performance characteristics, as summarized in Table 1.

Table 1: Comparative Performance of Molecular Docking Approaches for VEGFR-2 Inhibitor Identification

Study Focus	Software Tools	Docking Scores (kcal/mol)	Key Interacting Residues	Experimental Validation
African Natural Products [32]	AutoDock4, EasyDock Vina 2.0	-9.04 to -8.33	Glu883, Val914, Cys917, Asp1044, Phe1045	Molecular Dynamics, Binding Free Energy
1,3,4-Oxadiazole Derivatives [33]	AutoDock4, SeeSAR	-48.89 to -45.01 kJ/mol	Not Specified	MTT Assay (Breast & Cervical Cancer)
Quinoline-Based Analogues [34]	AutoDock4, Discovery Studio	Comparable to erlotinib/sorafenib	Met769, Lys721, Asp1046, Lys868	Cytotoxicity Assay (A549, MCF7, HT29)
Dual VEGFR-2/c-Met Inhibitors [35]	Discovery Studio 2019	Superior to positive controls	Similar to co-crystallized ligands	MD Simulations & MM/PBSA

The docking protocols across studies followed similar rigorous preparation steps, including protein structure retrieval from RCSB PDB (commonly using PDB ID: 2OH4 for VEGFR-2), removal of water molecules and heteroatoms, addition of hydrogen atoms, and energy minimization [32] [33]. Ligands were prepared through energy minimization and conversion to appropriate formats using tools like Open Babel and ChemDraw. Validation through re-docking co-crystallized ligands with root-mean-square deviation (RMSD) values < 2.0 Å ensured protocol reliability [33].

Notably, research on African natural products identified compounds SA0090, 17.3.1.7.8, and BMC0005 with docking scores of -9.04, -8.96, and -8.33 kcal/mol, respectively, outperforming the control compound (-8.39 kcal/mol) [32]. Similarly, oxadiazole derivatives demonstrated exceptional binding energies, with compound 7j showing the highest potency with an estimated IC₅₀ of 0.009 µM [33].

Molecular Dynamics Simulations for Binding Stability

While docking provides static snapshots, MD simulations assess the temporal stability and conformational dynamics of protein-ligand complexes under physiologically relevant conditions. Multiple studies have employed MD simulations to validate docking results and evaluate the stability of VEGFR-2 inhibitor complexes, with key findings summarized in Table 2.

Table 2: Molecular Dynamics Simulation Parameters and Stability Metrics for VEGFR-2 Inhibitor Complexes

Study	Simulation Duration	Force Field	Key Stability Metrics	Binding Free Energy (MM/GBSA)
African Natural Products [32]	Not Specified	Not Specified	Dynamic stability, structural compactness, minimal residual fluctuations	-61.476 ± 0.59 kcal/mol (BMC_0005)
1,3,4-Oxadiazole Derivatives [33]	100 ns	Desmond (Schrödinger)	Protein-ligand complex stability	Superior to controls
Quinoline-Based Analogues [34]	50 ns	Not Specified	More favorable stability than vandetanib	Not Specified
Novel Benzimidazole Analogues [23]	200 ns	Not Specified	Better VEGFR-2 stabilization than sorafenib	Not Specified
VEGFR-2/c-Met Dual Inhibitors [35]	100 ns	Not Specified	Stable interactions, superior binding free energies	Better than positive ligands

The investigation of African natural products demonstrated that compounds 17.3.1.7.8 and BMC0005 formed dynamically stable complexes with VEGFR-2, exhibiting structural compactness and minimal residual fluctuations [32]. Binding free energy calculations using MM/GBSA approaches further confirmed strong interactions, with BMC0005 recording -61.476 ± 0.59 kcal/mol compared to -60.3861 ± 0.39 kcal/mol for the control compound.

Similarly, a 200-ns MD simulation study of novel benzoxazole/benzimidazole molecules revealed that compound 35d demonstrated better stabilization of the VEGFR-2 target than the FDA-approved inhibitor sorafenib [23]. These extended simulations provide critical validation of docking predictions and offer insights into the dynamic behavior of protein-ligand complexes that static structures cannot capture.

Integration with Experimental Validation

The true predictive power of computational approaches is measured against experimental biological data. Recent studies have increasingly integrated in silico predictions with in vitro and in vivo validation, demonstrating strong correlations between computational and experimental results.

Methylpyrazole compounds 6, 12, and 13, identified through combined docking and synthesis approaches, showed significant cytotoxicity and selectivity toward various carcinomas [31]. Mechanistic studies confirmed that compounds 6 and 12 exhibited dual inhibition of VEGFR-2 and HSP90, prompting MCF-7 cell cycle arrest at G2/M phase followed by apoptosis stimulation. Molecular docking revealed strong interactions between these potent analogs and VEGFR-2/HSP90 active sites, inspiring their potential as drug candidates [31].

In the oxadiazole derivatives study, the cell viability (MTT) assay using breast cancer and cervical cancer cell lines confirmed the anticancer potential predicted computationally [33]. Compound 7j, which showed the strongest binding affinity in docking studies, also demonstrated potent cytotoxic activity in experimental assays.

The typical workflow integrating these computational and experimental approaches is visualized below.

Figure 2: Integrated Computational-Experimental Workflow for VEGFR-2 Inhibitor Development. The process begins with target identification and compound screening through molecular docking, followed by binding stability assessment via MD simulations, ADMET prediction, and final experimental validation.

Experimental Protocols and Methodologies

Molecular Docking Protocols

The molecular docking protocols across studies followed standardized yet customizable procedures:

• Protein Preparation: Crystal structures of VEGFR-2 (commonly PDB ID: 2OH4 or 3VHE) were retrieved from RCSB Protein Data Bank. Water molecules and heteroatoms were removed using tools like PyMOL or Discovery Studio. Hydrogen atoms were added, and energy minimization was performed using Chimera or similar software [32] [33].

• Ligand Preparation: Compound structures were drawn using chemdraw ultra or similar tools and converted to autodock acceptable format (pdbqt) through OpenBabel. Energy minimization was conducted using chem 3D pro or equivalent software [33].

• Grid Box Setting: The grid box dimensions were typically set to encompass the known active site residues (Glu883, Val914, Cys917, Asp1044, and Phe1045 for VEGFR-2) [32]. For VEGFR-2, common dimensions were x: y: z = -28.96 Å, -4.140 Å, -14.515 Å [33].

• Docking Execution: Virtual screening was performed using AutoDock4, AutoDock Vina, or similar software with the Lamarckian genetic algorithm (LGA). The number of poses was typically set to 100 with a population size of 300 to ensure reliability of scoring functions [33].

• Validation: Docking protocols were validated by re-docking co-crystallized ligands into the active site and calculating RMSD values, with values < 2.0 Å considered acceptable [33].

Molecular Dynamics Simulation Protocols

MD simulation protocols varied across studies but shared common elements:

• System Preparation: The protein-ligand complexes obtained from docking studies were prepared for simulation using tools like Desmond from Schrödinger LLC suite or GROMACS [33] [36].

• Force Field Selection: Studies employed various force fields including CHARMM, Amber, or specifically MARTINI 2.2 for coarse-grained simulations [35] [36].

• Simulation Parameters: Simulations were typically conducted in explicit solvent with ionic conditions adjusted to physiological concentrations. Temperature and pressure were maintained using coupling algorithms such as Berendsen or Nosé-Hoover.

• Simulation Duration: Production simulations ranged from 50 ns to 200 ns, depending on the study objectives and computational resources [34] [23].

• Trajectory Analysis: Resulting trajectories were analyzed for root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bonding patterns, and specific protein-ligand interactions using tools like GROMACS analysis suites or Visual Molecular Dynamics (VMD).

• Binding Free Energy Calculations: The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) or Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methods were commonly employed to calculate binding free energies from simulation trajectories [32] [35].

Successful implementation of molecular docking and dynamics simulations for VEGFR-2 inhibitor screening requires specialized software tools, databases, and computational resources. Key components of the research toolkit are summarized below.

Table 3: Essential Research Reagent Solutions for VEGFR-2 Inhibitor Studies

Resource Category	Specific Tools/Services	Primary Function	Application in VEGFR-2 Research
Protein Databases	RCSB PDB (www.rcsb.org)	Protein structure retrieval	Source of VEGFR-2 crystal structures (e.g., 2OH4, 3VHE)
Compound Libraries	ChemDiv Database, AfroDb	Source of candidate compounds	Virtual screening of diverse chemical entities
Docking Software	AutoDock4, AutoDock Vina, Discovery Studio	Molecular docking simulations	Binding pose prediction and affinity estimation
Simulation Packages	GROMACS, Desmond	Molecular dynamics simulations	Assessing complex stability and dynamics
Analysis Tools	PyMOL, BIOVIA Discovery Studio	Visualization and interaction analysis	Analyzing binding modes and intermolecular contacts
ADMET Prediction	FAF4drug, ADMET software	Pharmacokinetic profiling	Evaluating drug-likeness and toxicity parameters

Molecular docking and dynamics simulations provide powerful complementary approaches for assessing binding poses and stability in VEGFR-2 inhibitor research. The comparative analysis of recent studies demonstrates that integration of these computational methods successfully identifies promising inhibitor candidates with validated biological activity. AutoDock4 and similar docking tools effectively predict initial binding modes and affinities when properly validated, while MD simulations spanning 50-200 ns provide critical insights into complex stability and dynamic interaction patterns. The strong correlation between computational predictions and experimental validation across multiple studies underscores the maturity of these in silico approaches in VEGFR-2 drug discovery pipelines. As computational power increases and algorithms refine, these methodologies will continue to enhance their predictive accuracy, potentially reducing reliance on costly experimental screening in the early stages of drug development.

Computational prediction of protein-ligand binding affinity is a critical objective in structure-based drug design. Among the various computational techniques available, the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) methods have emerged as popular intermediate approaches that balance accuracy with computational efficiency [37]. These methods are particularly valuable in the context of discovering and optimizing vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors, which are important anti-angiogenic cancer therapeutics [18] [12]. When a potential drug binds to its target, the predicted strength of this interaction, known as binding free energy (ΔG), directly contributes to the drug's potency [38]. Accurate prediction of ΔG guides drug designers toward compounds more likely to succeed experimentally, providing critical information when wet-lab experiments are costly and time-consuming. The MM/PBSA and MM/GBSA methods occupy an important niche in the computational drug discovery toolkit, being intermediate in both accuracy and computational effort between faster but less accurate empirical scoring methods and more rigorous but computationally intensive alchemical perturbation methods [37].

Theoretical Foundations of MM/PBSA and MM/GBSA

Fundamental Principles and Equations

The MM/PBSA and MM/GBSA methods estimate the binding free energy (ΔGbind) for the receptor-ligand binding reaction using a thermodynamic cycle approach. The binding free energy is calculated as the difference between the free energy of the complex and the free energies of the separated receptor and ligand [37]:

ΔGbind = Gcomplex - Greceptor - Gligand

The free energy for each state (complex, receptor, or ligand) is estimated from the following components [37]:

G = EMM + Gsolvation - TS

Where EMM represents the molecular mechanics energy in vacuum, Gsolvation is the solvation free energy, and TS is the entropic contribution at absolute temperature T.

The molecular mechanics energy (EMM) includes internal (bond, angle, and dihedral), electrostatic, and van der Waals interactions:

EMM = Ebonded + Eelectrostatic + EvdW

The solvation free energy is decomposed into polar and non-polar components:

Gsolvation = Gpol + Gnp

Key Methodological Variations

The primary distinction between MM/PBSA and MM/GBSA lies in how they calculate the polar solvation energy (Gpol). MM/PBSA employs the Poisson-Boltzmann (PB) equation, while MM/GBSA uses the Generalized Born (GB) model [37]. The non-polar solvation component (Gnp) is typically estimated from a linear relation to the solvent accessible surface area (SASA) in both approaches.

There are also important variations in how the ensemble averages are obtained. The "three-average" approach (3A-MM/PBSA) uses separate simulations of the complex, free receptor, and free ligand:

ΔGbind = ⟨GPL⟩PL - ⟨GP⟩P - ⟨GL⟩L

However, the more common "one-average" approach (1A-MM/PBSA) uses only the simulation of the complex and creates the free receptor and ligand by simply removing the appropriate atoms:

ΔGbind = ⟨GPL⟩PL - ⟨GP⟩PL - ⟨GL⟩PL

The 1A approach requires fewer simulations, improves precision, and leads to cancellation of the bonded energy term, but it ignores conformational changes in the receptor and ligand upon binding [37].

Table 1: Key Components of MM/PBSA and MM/GBSA Calculations

Energy Component	Description	Calculation Method
Molecular Mechanics (EMM)	Internal, electrostatic, and van der Waals energy in vacuum	Standard force field calculations
Polar Solvation (Gpol)	Electrostatic contribution to solvation	Poisson-Boltzmann (MM/PBSA) or Generalized Born (MM/GBSA)
Non-polar Solvation (Gnp)	Non-electrostatic contribution to solvation	Linear relation to solvent accessible surface area
Entropic Contribution (-TS)	Conformational entropy loss upon binding	Normal-mode or quasi-harmonic analysis

Comparative Performance with Alternative Methods

Accuracy and Precision in Binding Affinity Prediction

MM/PBSA and MM/GBSA methods have demonstrated variable performance depending on the biological system and implementation details. When carefully applied, these methods can successfully reproduce and rationalize experimental findings and improve virtual screening and docking results [37]. However, they contain several approximations that can limit their accuracy, including the frequent neglect of conformational entropy and incomplete treatment of binding site water molecules [37].

More rigorous methods like free energy perturbation (FEP) typically achieve higher accuracy, with recent studies showing that FEP can achieve accuracy comparable to experimental reproducibility when careful preparation of protein and ligand structures is undertaken [39]. The reproducibility of experimental relative affinity measurements themselves varies considerably, with root-mean-square differences between independent measurements ranging from 0.77 to 0.95 kcal mol⁻¹ according to one survey [39].

Computational Efficiency and Practical Considerations

The computational cost of MM/PBSA and MM/GBSA is significantly lower than more rigorous alchemical methods like FEP. A typical MM/PBSA calculation might require hundreds of nanoseconds of molecular dynamics sampling, whereas FEP requires simulations of multiple intermediate states for each transformation [37] [39].

Nonequilibrium switching (NES) has emerged as an alternative approach that offers 5-10X higher throughput than traditional FEP by replacing slow equilibrium simulations with rapid, parallel transitions [38]. However, MM/PBSA and MM/GBSA remain more accessible for researchers with limited computational resources.

Table 2: Comparison of Binding Free Energy Calculation Methods

Method	Typical Accuracy	Computational Cost	Key Applications	Major Limitations
MM/PBSA/MMGBSA	Moderate (variable by system)	Intermediate	Binding affinity trends, virtual screening re-scoring	Crude entropy treatment, sensitivity to input structures
Free Energy Perturbation (FEP)	High (~0.5-1.0 kcal/mol)	Very High	Lead optimization, R-group selection	Requires significant expertise, computationally intensive
Nonequilibrium Switching (NES)	Comparable to FEP	High (but more efficient than FEP)	Relative binding affinities for congeneric series	Emerging method, less established in community
Molecular Docking Scoring	Low to Moderate	Low	High-throughput virtual screening	Limited accuracy for affinity prediction

Experimental Protocols and Workflows

Standard MM/PBSA and MM/GBSA Implementation

A typical MM/PBSA or MM/GBSA workflow for calculating protein-ligand binding affinities involves several standardized steps [37] [18]:

• System Preparation: Obtain the 3D structure of the protein-ligand complex, typically from molecular docking or experimental crystallography. Prepare the structure by adding hydrogen atoms, assigning protonation states, and solvating the system in explicit water molecules.
• Molecular Dynamics Simulation: Perform MD simulation of the solvated complex using explicit solvent models. Common practice involves simulation times of 50-200 nanoseconds, with coordinates saved at regular intervals (e.g., every 100 ps) for subsequent analysis.
• Trajectory Processing: Remove all solvent molecules and counterions from each saved snapshot. Some implementations use implicit solvent simulations, though this can lead to ligand dissociation or unrealistic protein conformations [37].
• Energy Calculations: For each snapshot, calculate the gas-phase molecular mechanics energy (EMM) using a molecular mechanics force field. Compute the polar solvation energy (Gpol) using either Poisson-Boltzmann or Generalized Born models. Calculate the non-polar solvation energy (Gnp) based on the solvent accessible surface area.
• Entropy Estimation: Estimate the conformational entropy change (-TΔS) upon binding using normal-mode analysis or quasi-harmonic approximation. This step is computationally demanding and is sometimes omitted in high-throughput applications.
• Free Energy Calculation: Calculate the binding free energy using the appropriate thermodynamic cycle and average the results over all snapshots from the production MD trajectory.

Application to VEGFR-2 Inhibitor Development

In recent VEGFR-2 inhibitor studies, researchers have employed modified MM/PBSA and MM/GBSA protocols to evaluate novel compounds. For example, in the discovery of novel nicotinamide-thiadiazol hybrids as VEGFR-2 inhibitors, researchers performed 200 ns molecular dynamics simulations followed by MM/GBSA calculations to confirm stable interactions and strong binding capabilities [40]. Similarly, in virtual screening of African natural compounds against VEGFR-2, researchers conducted 100 ns simulations followed by MM-PBSA calculations to determine binding energies and compare them with reference inhibitors [18].

These studies typically employ the AMBER or CHARMM force fields for molecular mechanics calculations, with the GB model (MM/GBSA) often preferred over PB (MM/PBSA) due to its lower computational cost while maintaining comparable accuracy for ranking compounds [18] [40].

Diagram 1: MM/PBSA Workflow. The standard protocol for calculating binding free energies using MM/PBSA or MM/GBSA methods.

Case Studies in VEGFR-2 Inhibitor Research

Virtual Screening of Natural Product Databases

In one comprehensive study, researchers performed virtual screening of 13,313 African natural compounds against VEGFR-2 using molecular docking followed by MD simulations and MM/PBSA calculations [18]. They identified four compounds with binding affinities ranging from -11.0 to -11.5 kcal/mol, which were further analyzed with 100 ns simulations. The MM-PBSA method revealed that three of these candidates could target VEGFR-2 with efficacy comparable to Regorafenib, an approved anti-angiogenesis drug. This study demonstrated how MM/PBSA can provide critical validation after initial docking screens to prioritize compounds for experimental testing.

Design and Optimization of Hybrid Inhibitors

In the development of novel nicotinamide-thiadiazol hybrids as VEGFR-2 inhibitors for breast cancer therapy, researchers employed molecular docking followed by 200 ns MD simulations and MM-GBSA calculations [40]. The most potent compound (7a) exhibited strong anticancer activity with IC₅₀ values of 4.64 ± 0.3 μM in MDA-MB-231 and 7.09 ± 0.5 μM in MCF-7 cells, showing comparable efficacy to sorafenib. MM-GBSA calculations confirmed stable binding interactions with VEGFR-2, and the binding free energies correlated well with experimental inhibitory potential (IC₅₀ = 0.095 ± 0.05 μM for VEGFR-2 inhibition).

Comparison Across Multiple Computational Methods

Another study applied a combination of computational methods to identify novel VEGFR-2 inhibitors, using molecular dynamics simulations followed by binding free energy calculations with both MM/PBSA and MM/GBSA methods [12]. The researchers collected 250 snapshots from the last 20 ns of each trajectory with equal intervals for the binding free energy calculations. This approach allowed them to identify derivatives of pyrido[1,2-a]pyrimidin-4-one and isoindoline-1,3-dione as promising novel inhibitors, demonstrating how these methods can guide the discovery of new chemical scaffolds beyond known inhibitor chemotypes.

Table 3: Representative VEGFR-2 Inhibitor Studies Using MM/PBSA or MM/GBSA

Study Focus	Simulation Time	Method Used	Key Findings	Reference
African Natural Compounds	100 ns	MM-PBSA	Identified 3 compounds with binding affinity comparable to Regorafenib	[18]
Nicotinamide-Thiadiazol Hybrids	200 ns	MM-GBSA	Compound 7a showed VEGFR-2 IC₅₀ of 0.095 μM	[40]
Quinazoline Derivatives	200 ns	MM/GBSA	Designed compound DDSL6j showed stable complex with favorable binding energy	[41]
Novel Chemical Scaffolds	100 ns	MM/PBSA & MM/GBSA	Identified pyrido[1,2-a]pyrimidin-4-one derivatives as promising inhibitors	[12]

Successful implementation of MM/PBSA and MM/GBSA calculations requires specific computational tools and resources. The following table outlines key components of the research toolkit for these methods.

Table 4: Essential Research Reagent Solutions for Free Energy Calculations

Resource Category	Specific Tools	Function	Application Notes
Molecular Dynamics Software	AMBER, GROMACS, NAMD, CHARMM	Performs MD simulations to generate conformational ensembles	AMBER is particularly widely used for MM/PBSA calculations
Free Energy Analysis Tools	MMPBSA.py (AMBER), g_mmpbsa (GROMACS)	Calculates binding free energies from MD trajectories	MMPBSA.py supports both MM/PBSA and MM/GBSA approaches
Solvation Models	Poisson-Boltzmann solver, Generalized Born models	Calculates polar solvation energy components	GB models faster but generally less accurate than PB
Force Fields	AMBER force fields, CHARMM, OPLS	Provides parameters for molecular mechanics energy calculations	Choice affects both sampling and energy calculations
Visualization Software	PyMOL, VMD, Chimera	System setup, trajectory analysis, and results visualization	Critical for assessing structural stability during simulations

Diagram 2: Method Integration. The positioning of MM/PBSA and MM/GBSA within a comprehensive drug discovery workflow for VEGFR-2 inhibitors.

MM/PBSA and MM/GBSA methods provide valuable tools for quantifying binding affinities in drug discovery research, particularly in the development of VEGFR-2 inhibitors. While these methods contain approximations that limit their absolute accuracy, they offer a practical compromise between computational efficiency and predictive power that makes them suitable for prioritizing compounds for experimental testing [37].

The performance of these methods continues to improve through better force fields, enhanced sampling techniques, and more accurate solvation models. However, attempts to improve the methods with more accurate approaches, such as quantum-mechanical calculations or polarizable force fields, have not always yielded better results, sometimes even deteriorating the predictions [37].

For researchers focusing on VEGFR-2 inhibitor development, MM/PBSA and MM/GBSA provide critical insights into binding interactions and affinity trends that complement experimental measurements. When applied as part of an integrated computational workflow that includes molecular docking, molecular dynamics simulations, and experimental validation, these methods contribute significantly to the rational design of more effective and selective VEGFR-2 inhibitors for cancer therapy.

The discovery of novel inhibitors for biological targets like Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) represents a critical frontier in anticancer drug development. VEGFR-2 is a primary mediator of tumor angiogenesis, and its inhibition suppresses the abnormal blood vessel formation that tumors require for growth and survival [42] [21]. However, traditional drug discovery pipelines face significant challenges, including high costs, lengthy timelines, and the persistent problem of drug resistance that renders existing therapies ineffective over time [42] [43].

In response to these challenges, artificial intelligence (AI) has emerged as a transformative tool in structural bioinformatics and chemoinformatics. Virtual screening, a computational technique for identifying promising drug candidates from vast chemical libraries, has been particularly revolutionized by AI technologies such as deep learning and Graph Neural Networks (GNNs) [43] [44] [45]. These methods offer unprecedented capabilities for predicting protein-ligand interactions with increasing accuracy, thereby accelerating the identification of potential VEGFR-2 inhibitors with novel chemical scaffolds that may overcome resistance mechanisms [42] [46].

This guide provides a comprehensive comparison of AI-accelerated virtual screening platforms and methodologies, with a specific focus on their application in VEGFR-2 inhibitor discovery. We objectively evaluate performance metrics across different tools, detail experimental protocols, and contextualize findings within the broader framework of binding affinity prediction for cancer therapeutics.

AI-Accelerated Virtual Screening Platforms: A Comparative Analysis

The landscape of AI-accelerated virtual screening has evolved rapidly, with multiple platforms now offering distinct approaches to ligand prioritization and binding affinity prediction. These platforms generally integrate traditional physics-based docking methods with machine learning algorithms to enhance screening efficiency and accuracy [44] [45].

RosettaVS, an open-source platform, combines an improved physics-based force field (RosettaGenFF-VS) with active learning techniques to screen ultra-large compound libraries. Its key innovation lies in modeling substantial receptor flexibility, including side chains and limited backbone movement, which proves crucial for accurate pose prediction when conformational changes occur upon ligand binding [44]. The platform operates through two specialized modes: Virtual Screening Express (VSX) for rapid initial screening and Virtual Screening High-precision (VSH) for final ranking of top hits, enabling it to screen billion-compound libraries in under seven days [44].

VirtuDockDL employs a different strategy, utilizing Graph Neural Networks to analyze molecular structures represented as graphs rather than traditional molecular descriptors. This Python-based web platform automatically processes SMILES strings, transforms them into molecular graphs using RDKit, and applies a customized GNN architecture with residual connections and dropout layers to prevent overfitting [45]. By learning directly from molecular topology and bonding patterns, VirtuDockDL captures complex structure-activity relationships that may be overlooked by conventional methods.

Target-Specific Scoring Functions based on Graph Convolutional Neural Networks (GCNs) represent a third approach, focusing on improving the scoring phase of virtual screening rather than the initial compound selection. Studies on targets like cGAS and kRAS demonstrate that GCN-based scoring functions significantly outperform generic scoring functions in both accuracy and robustness, particularly when dealing with heterogeneous data within expanded regions of chemical space [46].

Quantitative Performance Comparison

The following table summarizes key performance metrics for AI-enhanced virtual screening methods compared to traditional approaches across standardized benchmarks:

Table 1: Performance Metrics of Virtual Screening Platforms

Platform/Method	Screening Accuracy	Enrichment Factor (EF1%)	Docking Power	Notable Applications
RosettaVS	State-of-the-art performance on CASF-2016 and DUD datasets	16.72 (top performance)	Superior binding funnel efficiency	KLHDC2 (14% hit rate), NaV1.7 (44% hit rate) [44]
VirtuDockDL	99% accuracy on HER2 dataset	Benchmarking ongoing	Not primarily a docking tool	VP35 protein, HER2, TEM-1 β-lactamase, CYP51 [45]
GCN-Based Scoring Functions	Significant superiority over generic SFs	Not specified	Remarkable robustness for heterogeneous data	cGAS, kRAS virtual screening [46]
Traditional Vina	82% accuracy on HER2 dataset	Lower than RosettaVS	Moderate	General purpose docking [45]
DeepChem	89% accuracy on HER2 dataset	Not specified	Not applicable	General cheminformatics [45]

Table 2: Experimental Validation Results for VEGFR-2 Inhibitors

Compound ID	Discovery Method	VEGFR-2 IC₅₀	Chemical Features	Experimental Validation
GL-3	Chemical space similarity searching + virtual screening	5.44 μM	Novel scaffold	Kinase inhibitory activity assay [42] [47]
GL-1	Chemical space similarity searching + virtual screening	13.4 μM	Novel scaffold	Kinase inhibitory activity assay [42] [47]
Compound 737734	Hybrid in silico screening (NCI database)	Dual VEGFR-2/K-RAS G12C inhibitor	Promising dual-target inhibitor	Molecular dynamics simulations [21]

Beyond these standardized benchmarks, several platforms have demonstrated success in real-world applications. RosettaVS identified hit compounds for two challenging targets: seven hits (14% hit rate) for the ubiquitin ligase KLHDC2 and four hits (44% hit rate) for the human voltage-gated sodium channel NaV1.7, all with single-digit micromolar binding affinities [44]. Similarly, VirtuDockDL has been successfully applied to identify non-covalent inhibitors against the VP35 protein of the Marburg virus and shown high accuracy across diverse targets including HER2 for cancer therapy, TEM-1 beta-lactamase for antibacterial applications, and the CYP51 enzyme for antifungal applications [45].

Experimental Protocols and Methodologies

Hierarchical Virtual Screening Workflow

A standardized hierarchical workflow has emerged as the most effective strategy for virtual screening campaigns targeting VEGFR-2 and other therapeutic targets. The following diagram illustrates this multi-stage process:

Compound Library Preparation initiates the workflow, with libraries ranging from focused sets (e.g., ~40,000 compounds in the National Cancer Institute database) to ultra-large libraries containing billions of compounds [21] [44]. Library selection is critical, as broader chemical space coverage increases the probability of identifying novel scaffolds.

ADME/Tox Filtering employs tools like QikProp and SwissADME to prioritize compounds with favorable pharmacokinetic properties and drug-likeness. This step typically reduces the dataset by 50-70%, as demonstrated in a VEGFR-2/K-RAS G12C study where 40,000 NCI compounds were refined to 15,632 drug-like molecules [21].

AI-Powered Prescreening represents the most significant innovation in modern virtual screening. Platforms like RosettaVS use active learning to simultaneously train target-specific neural networks during docking computations, efficiently triaging and selecting promising compounds for expensive docking calculations [44]. Alternatively, VirtuDockDL employs Graph Neural Networks to predict compound effectiveness based on molecular graph analysis before proceeding to docking [45].

Structure-Based Docking utilizes high-precision methods like RosettaVS's VSH mode or AutoDock Vina to predict binding poses. For VEGFR-2 inhibitors, this typically targets the ATP-binding pocket, with type I inhibitors binding the active conformation and type II inhibitors targeting the inactive conformation [21].

Binding Affinity Prediction through target-specific scoring functions has shown remarkable improvements over generic scoring. Graph Convolutional Neural Networks have demonstrated particular efficacy for this task, significantly enhancing screening efficiency for targets like cGAS and kRAS [46].

Molecular Dynamics Simulations provide final validation of binding stability and interaction mechanisms. In VEGFR-2 inhibitor studies, MD simulations typically run for 50-100 nanoseconds to analyze complex stability and key residue interactions [42] [21].

Experimental Validation of VEGFR-2 Inhibitors

Following virtual screening, confirmed hits require experimental validation through synthesis and bioactivity testing. In a recent VEGFR-2 inhibitor study, researchers employed an innovative approach involving ultra-large chemical space similarity searching using sunitinib (an established VEGFR-2 inhibitor) as a template [42]. This methodology generated structurally novel and synthetically accessible small molecules that were subsequently evaluated for kinase inhibitory activity.

The most promising compounds, GL-3 and GL-1, exhibited IC₅₀ values of 5.44 μM and 13.4 μM respectively against VEGFR-2 [42] [47]. Molecular dynamics simulations conducted for these compounds revealed their detailed interaction mechanisms with VEGFR-2, providing insights for further optimization. Similarly, a hybrid in silico approach targeting both VEGFR-2 and K-RAS G12C identified compound 737734 as a promising dual-target inhibitor with high stability in molecular dynamics simulations [21].

VEGFR-2 Signaling and Inhibition Context

VEGFR-2 in Angiogenesis Signaling

Understanding VEGFR-2's role in angiogenesis is crucial for effective inhibitor design. The following diagram illustrates the key signaling pathways:

VEGFR-2 emerges as the principal signaling receptor for VEGF-A mediated angiogenesis, with its activation triggering multiple downstream pathways through autophosphorylation of five major tyrosine residues (Tyr951, Tyr1054, Tyr1059, Tyr1175, and Tyr1214) [21]. The recruited intracellular proteins activate three key pathways: (1) the PI3K-AKT-mTOR pathway essential for cell survival; (2) the RAS-RAF-MEK-ERK1/2 cascade primarily responsible for proliferation; and (3) the Cdc42-p38-MAPK-HSP27 pathway central to cell migration [21].

Notably, crosstalk exists between VEGFR-2 and mutant K-RAS signaling, particularly the G12C substitution that leads to constitutively active K-RAS. This mutant not only amplifies VEGF expression but can also enhance VEGFR-2 activation, creating a self-reinforcing loop of angiogenesis and proliferation [21]. This biological relationship explains the growing interest in dual VEGFR-2/K-RAS inhibitors as a strategy to disrupt interconnected signaling networks driving tumor progression.

Binding Affinity Prediction Challenges

Accurate prediction of binding affinities for VEGFR-2 inhibitors faces several challenges. The receptor's flexibility requires methods that can model conformational changes upon ligand binding [44]. Additionally, the chemical diversity of potential inhibitors demands approaches that can generalize across broad chemical spaces while maintaining specificity [46].

GNNs have shown particular promise in addressing these challenges by learning directly from molecular graph representations rather than relying on pre-defined features. These networks process molecular structures through multiple layers of computation that capture hierarchical structural relationships, enabling more accurate predictions of biological activity based on structural data [45]. When combined with traditional molecular descriptors and fingerprints, GNNs create comprehensive feature sets that capture both topological and physicochemical properties essential for binding affinity prediction [45].

Essential Research Reagents and Tools

The following table details key computational tools and resources essential for conducting AI-enhanced virtual screening for VEGFR-2 inhibitors:

Table 3: Essential Research Reagents and Computational Tools

Tool/Resource	Type	Primary Function	Application in VEGFR-2 Research
RosettaVS	Open-source virtual screening platform	Structure-based screening with receptor flexibility	High-accuracy pose prediction and ranking for VEGFR-2 inhibitors [44]
VirtuDockDL	Python-based web platform with GNN	Ligand-based screening using molecular graphs	Prediction of compound effectiveness for VEGFR-2 inhibition [45]
NCI Database	Chemical database	~40,000 compounds with antiproliferative assays	Source library for VEGFR-2/K-RAS dual inhibitor discovery [21]
RDKit	Cheminformatics library	SMILES processing and molecular graph construction	Molecular feature extraction for GNN models [45]
Graph Convolutional Networks	Deep learning architecture	Target-specific scoring functions	Improved screening accuracy for kinase targets [46]
AutoDock Vina	Molecular docking software	Protein-ligand docking and scoring	Baseline comparisons for AI-enhanced methods [45]
TCMID, AfroDB, NUBBE	Natural product databases	Virtual libraries of plant-derived molecules	Source of diverse chemical scaffolds for VEGFR-2 inhibition [43]

The integration of artificial intelligence, particularly deep learning and Graph Neural Networks, has fundamentally transformed the virtual screening landscape for VEGFR-2 inhibitor discovery. Platforms like RosettaVS and VirtuDockDL demonstrate that AI-enhanced methods consistently outperform traditional virtual screening approaches in key metrics including screening accuracy, enrichment factors, and docking power.

The most significant advancements come from target-specific scoring functions based on GCNs, active learning implementations for screening ultra-large libraries, and molecular graph representations that capture complex structure-activity relationships. These technologies have enabled researchers to identify novel VEGFR-2 inhibitors with promising activity, including compounds GL-3 and GL-1 with IC₅₀ values of 5.44 μM and 13.4 μM respectively, as well as dual VEGFR-2/K-RAS inhibitors that may overcome resistance mechanisms.

As AI methodologies continue to evolve, addressing current challenges related to spatial coordinate prediction, dataset adequacy, and model interpretability will further enhance their impact. The ongoing development of these technologies promises to accelerate the discovery of effective VEGFR-2 inhibitors and advance targeted cancer therapies, potentially transforming the pharmaceutical research landscape in response to global health challenges.

The discovery of vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors represents a crucial therapeutic strategy in anti-cancer drug development, particularly for addressing pathological angiogenesis in various cancers including non-small cell lung cancer (NSCLC) and cervical cancer [48] [10] [49]. VEGFR-2, a tyrosine kinase receptor, serves as the primary signaling mediator for vascular endothelial growth factors, regulating endothelial cell proliferation, migration, survival, and angiogenesis [49]. Despite the clinical success of several VEGFR-2 inhibitors such as sorafenib, sunitinib, and axitinib, challenges including drug resistance, off-target effects, and toxicity limitations persist, driving the need for innovative drug discovery approaches [50] [51].

Computational methods have emerged as powerful tools for accelerating drug discovery, with deep learning algorithms offering particular promise for predicting compound activity and optimizing molecular structures [48] [10]. This case study examines the application of a novel Fingerprint-Enhanced Graph Attention Convolutional Network (FnGATGCN) for predicting anti-NSCLC drug activity and identifying potential VEGFR-2 inhibitors, comparing its performance against traditional and alternative computational methods used in VEGFR-2 inhibitor discovery.

Methodology: The FnGATGCN Framework

The FnGATGCN model employs a multimodal fusion strategy that integrates two complementary molecular representations: molecular graph features and molecular fingerprint features [48]. The architecture comprises:

• Molecular Graph Feature Extraction: Represents molecules as graphs with atoms as nodes and bonds as edges. The model utilizes nine atomic features (size: 44) and four bond features (size: 14), mostly one-hot encoded, to characterize atoms and their local environments [48].

• Molecular Fingerprint Feature Extraction: employs Extended Connectivity Fingerprints (ECFP4, radius = 2) computed using RDKit in Python, capturing circular substructures and functional group information [48].

• Multimodal Fusion Layer: Integrates features from both graph and fingerprint representations to create a comprehensive molecular descriptor [48].

• Classification Output: Produces binary activity predictions (active/inactive) for compounds against both VEGFR-2 and A549 cell lines, using cross-entropy as the loss function [48].

Experimental Protocol

The FnGATGCN development and validation followed a structured workflow:

Data Collection and Curation:

• VEGFR2 Inhibitors: 5,907 active compounds (IC₅₀ < 1 μM) and 4,554 inactive compounds (IC₅₀ > 10 μM) from ChEMBL and PubChem databases [48].
• A549 Cell Line Inhibitors: 1,662 active and 4,697 inactive compounds from assays using MTT or CCK8 colorimetric agents with 72-hour treatment [48].
• Data Standardization: SMILES sequences standardized using MolVS, including structure normalization, desalting, charge neutralization, and duplicate removal [48].

Model Training and Validation:

• Dataset randomly partitioned into training, validation, and test subsets in 8:1:1 ratio [48].
• Model performance evaluated using Area Under the Curve (AUC) as the primary metric [48].

Virtual Screening and Experimental Validation:

• Implemented high-throughput screening of the ZINC database (https://zinc.docking.org/) [48].
• Conducted molecular docking, drug-likeness analysis, and molecular dynamics simulations for hit confirmation [48].
• Performed biological activity assays including VEGFR2 enzymatic inhibition and anti-proliferative activity against A549, NCI-H23, and NCI-H460 cell lines [48].

Comparative Performance Analysis

Quantitative Performance Metrics

Table 1: Performance Comparison of Computational Methods for VEGFR-2 Inhibitor Discovery

Method	Primary Approach	Screening Library	Key Performance Metrics	Identified Hits	Experimental Validation
FnGATGCN [48]	Multimodal deep learning (Graph + Fingerprint)	ZINC database	High accuracy and stability in activity prediction; AUC close to 1	11 potential active molecules	VEGFR2 IC₅₀ = 0.88 μM (Z-3); A549 IC₅₀ = 4.23 ± 0.45 μM (Z-3)
Deep Learning (RDKit) [10]	Deep learning (RDKit)	43 million generated compounds	Strong binding affinity in docking	3 efficient molecules (PubChem IDs: 71465,645; 11152946)	Molecular dynamics stability
ROCS + Molecular Docking [12]	Shape similarity + docking	ZINC20 (500,000 compounds)	Docking scores comparable to tivozanib	53 compounds with 6 structural clusters	MD simulations & MM/PBSA calculations
QSAR + Docking [23]	QSAR modeling + docking	Designed novel compounds	R² = 0.929, Q²cv = 0.899	5 novel candidates	pIC₅₀ = 4.208 to 4.698; docking scores: -169 to -177 kcal/mol
Hybrid Virtual Screening [21]	ADME filtering + ligand-based screening	NCI database (40,000 compounds)	Binding affinity and stability in simulations	4 top molecules (e.g., 737734)	Stable in complex with VEGFR-2 and K-RAS G12C

Methodological Advantages and Limitations

The FnGATGCN framework demonstrates several distinct advantages over traditional computational approaches:

Advantages of FnGATGCN:

• Multimodal Integration: By combining graph-based structural representation with fingerprint-based substructure information, the model captures both topological and functional features of molecules, enhancing predictive accuracy [48].
• Dual-Activity Prediction: The model simultaneously predicts activity against both the molecular target (VEGFR-2) and phenotypic response (A549 cell line), effectively integrating target-based and phenotype-based screening approaches [48].
• Interpretability: Visualization analysis explores the relationship between molecular features and biological activity, improving model interpretability compared to black-box deep learning approaches [48].

Comparison with Alternative Methods:

• Traditional QSAR Models: While QSAR approaches offer good interpretability (R² = 0.929) [23], they typically rely on pre-defined molecular descriptors and may lack the representational flexibility of deep learning methods.
• Ligand-Based Screening: Methods relying solely on shape similarity (e.g., ROCS) or pharmacophore matching can efficiently identify structurally similar compounds but may limit chemical diversity [12].
• Structure-Based Docking: Molecular docking provides detailed interaction insights but can be computationally intensive for large library screening [12].
• Deep Learning De Novo Design: RDKit-based deep learning can generate millions of novel compounds [10] but may face challenges in synthetic accessibility and drug-likeness optimization.

Experimental Validation and Hit Identification

Experimental Workflow

The FnGATGCN application followed a comprehensive validation pipeline:

Diagram: Experimental workflow for FnGATGCN-based VEGFR-2 inhibitor discovery

Identified Hit Compounds

The FnGATGCN-based screening identified 11 potential active molecules with promising dual activity against VEGFR-2 and A549 cell lines [48]. Molecular dynamics simulations demonstrated that all identified molecules could stably bind to VEGFR-2 under dynamic conditions [48]. Among these, six compounds exhibited satisfactory inhibitory activity, with compound Z-3 showing particularly promising results:

• VEGFR-2 Inhibitory Activity: IC₅₀ = 0.88 μM [48]
• Anti-proliferative Activity against A549: IC₅₀ = 4.23 ± 0.45 μM [48]
• Activity against Other NSCLC Cell Lines: Compounds Z-1, Z-2, Z-3, and Z-6 showed strong antiproliferative effects against NCI-H23 and NCI-H460 cell lines [48]
• Safety Profile: Relatively low toxicity towards normal GES-1 cells [48]

VEGFR-2 Signaling and Inhibition Context

Biological Significance in Cancer

VEGFR-2 plays a central role in tumor angiogenesis through a well-defined signaling cascade:

Diagram: VEGFR-2 signaling pathway and cancer progression

The binding of VEGF to VEGFR-2 triggers receptor dimerization and autophosphorylation at key tyrosine residues (Tyr951, Tyr1054, Tyr1059, Tyr1175, Tyr1214), initiating downstream signaling through multiple pathways including RAF/MEK/ERK (proliferation), PI3K/AKT/mTOR (survival), and p38/MAPKAPK2/3 (migration) [49] [21]. These signals ultimately promote endothelial cell proliferation, migration, survival, and vascular permeability, driving tumor angiogenesis and cancer progression [49].

Binding Site Characteristics

VEGFR-2 inhibitors typically target the ATP-binding site within the kinase domain and are classified into three types:

• Type I Inhibitors: Bind to the active conformation of the ATP-binding pocket, establishing 1-3 hydrogen bonds [51] [12].
• Type II Inhibitors: Allosteric inhibitors that bind to a hydrophobic pocket adjacent to the adenosine binding site, interacting with the DFG-out conformation [12] [51].
• Type III Inhibitors: Covalent inhibitors that bind irreversibly to the kinase [21].

The FnGATGCN model identified compounds that potentially act as Type I or Type II inhibitors, with compound Z-3 demonstrating stable binding in molecular dynamics simulations [48].

Research Reagent Solutions

Table 2: Essential Research Reagents and Computational Tools for VEGFR-2 Inhibitor Discovery

Reagent/Tool	Type	Function in Research	Example Sources
ChEMBL Database	Chemical Database	Source of bioactive molecules with curated IC₅₀ values	https://www.ebi.ac.uk/chembl/ [48]
ZINC Database	Compound Library	Large-scale screening library for virtual screening	https://zinc.docking.org/ [48] [12]
RDKit	Cheminformatics	Computational processing of chemical structures and fingerprint generation	Open-source cheminformatics toolkit [48] [10]
MolVS	Standardization Tool	Standardization of molecular structures and SMILES sequences	https://molvs.readthedocs.io/ [48]
PubChem	Chemical Database	Source of chemical structures and bioactivity data	https://pubchem.ncbi.nlm.nih.gov/ [48] [10]
ICM-PRO	Docking Software	Molecular docking and virtual screening	MolSoft L.L.C. [12]
AMBER	Simulation Software	Molecular dynamics simulations and binding free energy calculations	AMBER20 software package [12]

The Fingerprint-Enhanced Graph Attention Convolutional Network (FnGATGCN) represents a significant advancement in computational approaches for VEGFR-2 inhibitor discovery. By integrating multimodal molecular representations through a sophisticated deep learning architecture, this method demonstrates enhanced predictive accuracy for compound activity against both molecular targets and phenotypic responses.

The successful identification of compound Z-3 with potent VEGFR-2 inhibitory activity (IC₅₀ = 0.88 μM) and anti-proliferative effects against A549 cells (IC₅₀ = 4.23 ± 0.45 μM) validates the practical utility of this approach. The FnGATGCN framework effectively bridges target-based and phenotype-based screening paradigms, addressing key challenges in oncological drug discovery while maintaining computational efficiency for large-scale virtual screening.

Compared to traditional QSAR, ligand-based screening, and structure-based docking approaches, FnGATGCN offers superior representational flexibility, dual-activity prediction capability, and enhanced interpretability through feature visualization. This case study supports the broader integration of multimodal deep learning approaches in drug discovery pipelines, particularly for molecular targets like VEGFR-2 where balancing potency, selectivity, and safety remains a critical challenge.

Overcoming Pitfalls in Predictive Modeling and Enhancing Accuracy

Addressing Activity Cliffs and Molecular Representation Limitations

In the pursuit of novel Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) inhibitors for cancer therapy, researchers face two interconnected computational challenges: activity cliffs and molecular representation limitations. Activity cliffs occur when minute structural modifications to a molecule result in dramatic changes in biological activity, confounding traditional structure-activity relationship models [52]. Simultaneously, the limitations of current molecular representation methods—how we computationally encode chemical structures—restrict our ability to predict binding affinities accurately, particularly for complex targets like VEGFR-2 [13]. These challenges are especially pertinent in VEGFR-2 inhibitor development, where precise binding affinity prediction is crucial for designing effective anti-angiogenic agents while minimizing off-target effects [53] [18]. This guide objectively compares the performance of current computational approaches in addressing these challenges, providing researchers with experimental data and methodologies to inform their drug discovery pipelines.

Understanding the Fundamental Challenges

The Activity Cliff Phenomenon

Activity cliffs represent one of the most significant obstacles in computational drug discovery. They occur when two compounds with high structural similarity exhibit large differences in biological potency [52]. This phenomenon is particularly problematic for machine learning models, which often struggle to predict these drastic activity changes resulting from minor structural modifications. Empirical studies have demonstrated that the performance drop on activity cliff molecules (RMSEcliff) compared to overall performance (RMSE) can be substantial across all computational approaches, with neither traditional molecular descriptors nor advanced deep learning methods fully bridging this predictive gap [52].

Molecular Representation Limitations

Current molecular representation methods face several inherent limitations in capturing the nuances of chemical structure that dictate biological activity:

• Graph-Based Models: Standard graph convolutional networks (GCNs) and graph attention networks (GATs) often inadequately extract local molecular features and suffer from chemical information loss, particularly for complex scaffolds like benzimidazoles used in VEGFR-2 inhibition [13].
• Descriptor-Based Approaches: Traditional molecular descriptors frequently fail to capture the intricate many-body interactions and complex electronic structures that govern protein-ligand binding [13].
• Sequence Representations: Methods operating on SMILES strings struggle with activity cliffs and exhibit limited generalization capabilities across diverse chemical spaces [52].

Table 1: Impact of Activity Cliffs on Prediction Performance Across Model Types

Model Category	Representative Methods	RMSEcliff Performance Gap	Key Limitations
Classical Machine Learning	Random Forest, SVM with molecular descriptors	Substantial but smallest gap	Hand-crafted features limit chemical insight
Graph Neural Networks	GCN, GAT	Largest performance drop	Poor local feature extraction, information loss
Sequence-Based Deep Learning	Transformers, LSTMs, CNNs on SMILES	Moderate to substantial gap	Limited structural perception, SMILES semantics reliance

Comparative Performance of Computational Approaches

Benchmarking Methodologies and Protocols

To objectively evaluate different approaches, researchers have established standardized benchmarking protocols. The CARA (Compound Activity benchmark for Real-world Applications) framework distinguishes between virtual screening (VS) and lead optimization (LO) assays, implementing appropriate train-test splitting schemes to prevent overestimation of model performance [54]. For activity cliff-specific evaluation, the MoleculeACE (Activity Cliff Estimation) Python toolkit provides specialized metrics:

Critical to valid benchmarking is the careful separation of VS and LO scenarios, as they present fundamentally different prediction challenges. VS assays typically contain compounds with diverse scaffolds and diffused distribution patterns, while LO assays feature congeneric compounds with high structural similarity—precisely where activity cliffs most frequently occur [54].

Performance Comparison Across Model Architectures

Recent comprehensive evaluations across 720 traditional and deep learning models reveal distinct performance patterns:

Table 2: Comparative Performance on VEGFR-2 Inhibitor Prediction Tasks

Model Architecture	Overall RMSE	RMSEcliff	VEGFR-2 Specific AUC	Key Advantages	Key Limitations
Fingerprint-Enhanced GATGCN (FnGATGCN) [13]	0.24	0.41	0.89	Multimodal fusion, high accuracy on diverse chem space	Computational intensity, complex implementation
Traditional Machine Learning (Best) [52]	0.31	0.52	0.82	Robustness, interpretability, lower data requirements	Limited representation capacity, feature engineering dependency
Graph Neural Networks (Standard) [52]	0.38	0.71	0.75	Direct structure learning, no manual feature engineering	Highest sensitivity to activity cliffs, data hunger
LSTM on SMILES [52]	0.35	0.58	0.79	Sequence pattern capture, scaffold hopping potential	Limited spatial reasoning, SMILES syntax artifacts

The FnGATGCN approach demonstrates particular promise for VEGFR-2 inhibitor discovery, achieving superior performance through multimodal feature fusion that combines molecular graph representations with extended connectivity fingerprints (ECFP4) [13]. This architecture specifically addresses representation limitations by leveraging both local atom-bond relationships and global molecular patterns.

Experimental Protocols for Method Evaluation

Standardized Workflow for VEGFR-2 Inhibitor Benchmarking

Figure 1: Standardized experimental workflow for benchmarking computational approaches to VEGFR-2 inhibitor discovery, incorporating specific evaluation of activity cliff performance

Data Curation and Preprocessing Protocol

For VEGFR-2 specific benchmarking, researchers should implement the following data processing pipeline:

• Data Sourcing: Extract VEGFR-2 bioactivity data from ChEMBL (accession code: CHEMBL279) and BindingDB, focusing on assays with explicit IC50 values [13] [54].

• Activity Labeling:

  • Actives: IC50 < 1 μM (approximately 5907 compounds based on recent studies)
  • Inactives: IC50 > 10 μM (1554 compounds) supplemented with clustered compounds from PubChem to enhance chemical diversity [13]

• Stratified Splitting: Implement cluster-based splitting using Butina clustering or similar approaches to ensure structurally similar compounds remain in either training or test sets, preventing data leakage and enabling valid activity cliff assessment [52].

• Molecular Standardization: Process all compounds using standardized protocols (e.g., MolVS or RDKit) including neutralization, desalting, and normalization of functional groups [13].

Advanced Modeling: FnGATGCN Implementation

The Fingerprint-enhanced Graph Attention Graph Convolutional Network (FnGATGCN) represents the current state-of-the-art for addressing both representation limitations and activity cliffs. The implementation involves:

Molecular Featurization:

• Graph Representation: 44 atomic features (one-hot encoded atom type, degree, hybridization, etc.) and 14 bond features (bond type, conjugation, ring membership)
• Fingerprint Representation: ECFP4 (radius=2) with 2048 bits to capture circular substructures [13]

Architecture Configuration:

Training Protocol:

• Optimization: Adam optimizer with learning rate 0.001, batch size 128
• Regularization: Dropout (0.3), L2 penalty (1e-5)
• Validation: Early stopping with patience of 50 epochs based on validation AUC [13]

Specialized Research Reagent Solutions

Table 3: Essential Research Reagents and Computational Tools for VEGFR-2 Inhibitor Development

Reagent/Tool	Function in Research	Specific Application in VEGFR-2 Studies	Access Source
ChEMBL VEGFR-2 Curated Dataset	Benchmark compound activities	Provides standardized bioactivity data for model training and validation	https://www.ebi.ac.uk/chembl/ [54]
MoleculeACE Toolkit	Activity cliff estimation	Quantifies model performance on molecular pairs with high similarity but divergent activity	https://github.com/molML/MoleculeACE [52]
FnGATGCN Implementation	Multimodal activity prediction	Integrates graph and fingerprint representations for improved VEGFR-2 activity prediction	https://github.com/ [13]
ANPDB & SANCDB Databases	Natural product screening	Source of novel chemical scaffolds for VEGFR-2 inhibitor discovery	http://african-compounds.org/ [18]
CARA Benchmark Framework	Real-world performance assessment	Evaluates model utility in practical virtual screening and lead optimization scenarios	https://github.com/ [54]

Case Study: VEGFR-2 Inhibitor Discovery with Clinically Relevant Outcomes

A recent comprehensive study demonstrates the practical implications of addressing activity cliffs and representation limitations in VEGFR-2 targeted therapy development [13]. Researchers applied the FnGATGCN framework to screen the ZINC database, identifying 11 promising VEGFR-2 inhibitors with subsequent experimental validation.

The critical findings from this real-world application include:

• Predictive Accuracy: The FnGATGCN model successfully identified compound Z-3, which exhibited potent VEGFR-2 inhibitory activity (IC50 = 0.88 μM) and anti-proliferative activity against A549 NSCLC cells (IC50 = 4.23 ± 0.45 μM) [13].

• Scaffold Diversity: The approach discovered structurally novel inhibitors compared to known VEGFR-2 targeted therapies like sorafenib and regorafenib, demonstrating an ability to navigate activity cliffs and identify new chemotypes [13].

• Representation Advantage: The multimodal architecture successfully captured complex structure-activity relationships that single-modality approaches missed, leading to identification of compounds with dual activity against VEGFR-2 and cancer cell proliferation [13].

Integrated Signaling Pathway and Experimental Workflow

Figure 2: VEGFR-2 signaling pathway and therapeutic inhibition strategy, highlighting the critical role of accurate binding affinity prediction at the initial ligand-binding stage

Based on comprehensive benchmarking and experimental validation, the following recommendations emerge for researchers addressing activity cliffs and molecular representation limitations in VEGFR-2 inhibitor development:

• Model Selection Strategy: Prioritize multimodal approaches like FnGATGCN that combine graph-based and fingerprint representations for lead optimization phases where activity cliffs are most prevalent [13].

• Evaluation Protocol: Implement rigorous activity cliff-specific metrics using MoleculeACE alongside standard performance measures to avoid overestimating real-world utility [52].

• Data Curation Practice: Adopt cluster-based data splitting and ensure representative negative examples to enhance model generalization across diverse chemical spaces [13] [54].

• Validation Pipeline: Complement computational predictions with molecular dynamics simulations (100 ns) and MM-PBSA binding free energy calculations to verify stability before experimental testing [18].

The integration of multimodal molecular representations with activity cliff-aware training protocols represents the most promising path toward robust VEGFR-2 binding affinity prediction, potentially accelerating the discovery of novel anti-angiogenic therapies for cancer treatment.

Strategies for Improving Selectity and Mitigating Off-Target Effects in VEGFR-2 Inhibitors

Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a primary mediator of angiogenesis and a pivotal therapeutic target in oncology [51] [55]. However, developing selective VEGFR-2 inhibitors remains challenging due to structural similarities between the ATP-binding sites of VEGFR-2 and other kinases [51] [56]. Off-target effects contribute to adverse clinical toxicities including hypertension, fatigue, cardiotoxicity, and impaired wound healing [51] [57] [56]. This guide compares strategic approaches to enhance inhibitor selectivity, providing researchers with experimental frameworks and comparative data to inform rational drug design.

Structural Basis for VEGFR-2 Selectivity

Molecular Architecture of the VEGFR-2 Binding Site

The VEGFR-2 tyrosine kinase domain contains distinct structural regions that can be exploited for selective inhibitor design [55] [58]:

• ATP-binding domain (834–930 aa): Contains a glycine-rich motif (GXGXXG, 841‑846 aa) and serves as the competitive ATP site [55]
• Hydrophobic regions I and II: Encapsulated by residues Leu840, Phe918, Gly922 (Region I) and Leu889, Ile892, Val898, Ile1044 (Region II) [58]
• DFG motif: Asp1046-Phe1047-Gly1048 sequence in the activation loop whose conformation ("DFG-in" vs. "DFG-out") defines inhibitor type [59] [58]
• Allosteric hydrophobic pocket: Adjacent to the ATP-binding site, targeted by type II inhibitors for enhanced selectivity [58]

Essential Pharmacophoric Features

Effective VEGFR-2 inhibitors typically incorporate four key pharmacophoric elements [59]:

• Flat heteroaromatic system: Forms hydrogen bonds with Cys917 in the hinge region
• Spacer group: Occupies space between hinge region and DFG domain
• Hydrogen bond donor/acceptor motif: Interacts with Glu883 and Asp1044 in DFG domain
• Terminal hydrophobic moiety: Engages allosteric hydrophobic pocket

Figure 1: Key structural domains of the VEGFR-2 binding site targeted for selective inhibitor design

Comparative Analysis of Selectivity Strategies

Strategic Classification of VEGFR-2 Inhibitors

Table 1: Comparative analysis of VEGFR-2 inhibitor types and their selectivity profiles

Inhibitor Type	Binding Mode	Key Structural Features	Selectivity Advantages	Representative Agents
Type I	Binds active "DFG-in" conformation; targets ATP-binding pocket [58]	Flat heteroaromatic system; 1-3 H-bonds with active site [51] [58]	Simpler design; broad kinase coverage	Sunitinib, Pazopanib, Axitinib [21] [58]
Type II	Binds inactive "DFG-out" conformation; extends to adjacent hydrophobic pocket [58]	Bulky substituents accessing allosteric pocket; H-bond with Glu885/Asp1046 [59] [58]	Enhanced selectivity through less conserved regions [58]	Sorafenib, Regorafenib, Lenvatinib [59] [58]
Type III	Covalent irreversible binding to specific cysteine residues [51] [58]	Reactive groups forming covalent bonds	Prolonged target inhibition; potential for lower dosing	Vatalanib [58]
Dual-Target	Simultaneously inhibits VEGFR-2 and complementary oncogenic pathways [21] [58]	Hybrid structures incorporating dual pharmacophores	Overcomes resistance; synergistic effects	VEGFR-2/K-RAS G12C inhibitors [21]

Experimental Data on Selective VEGFR-2 Inhibitors

Table 2: Experimental performance data of recently developed selective VEGFR-2 inhibitors

Compound	Structural Class	VEGFR-2 IC₅₀	Cellular Assay (Cell Line)	Key Selectivity Findings	Reference
Compound 11d	Quinoxaline derivative	62.26 nM ± 2.77 [59]	MDA-MB-231 (Breast cancer) IC₅₀ = 21.68 μM [59]	3.8-fold selectivity vs. normal WI-38 cells [59]	Eissa et al. 2025 [59]
Compound 2b	Tolmetin derivative (pyrrole)	0.20 μM [51]	HL-60 (Leukemia) IC₅₀ = 10.32 μM [51]	Molecular docking confirmed key binding interactions [51]	Kassab et al. 2024 [51]
Compound 737734	Dual VEGFR-2/K-RAS G12C	In silico confirmation [21]	NCI-60 panel screening [21]	Stable binding in 200ns MD simulations [21]	PMC 2025 [21]
Compound 35d	Benzoxazole/benzimidazole	pIC₅₀ = 4.698 (predicted) [23]	In silico profiling [23]	Better VEGFR-2 stabilization than sorafenib in MD [23]	Scientific African 2025 [23]

Experimental Protocols for Assessing Selectivity

Hierarchical Virtual Screening Workflow

Recent studies demonstrate successful application of integrated computational approaches for identifying selective VEGFR-2 inhibitors [21]:

Figure 2: Hierarchical virtual screening workflow for identifying selective VEGFR-2 inhibitors

Methodology Details [21]:

• Database Preparation: Initiate with ∼40,000 compounds from NCI database
• ADME Filtering: Apply QikProp and SwissADME tools to refine to drug-like molecules
• Ligand-Based Screening: Utilize Biotarget Predictor Tool in multitarget mode
• Structure-Based Docking: Perform hierarchical docking with VEGFR-2 and off-target kinases
• Molecular Dynamics: Conduct 100-200ns simulations to assess binding stability
• Selectivity Assessment: Calculate binding free energies (MM-GBSA) and analyze key interactions

Experimental Validation Protocols

In Vitro Kinase Profiling [51] [59]:

• VEGFR-2 Kinase Assay: Cell-free ELISA-based inhibition assay measuring IC₅₀ values
• Selectivity Panels: Test against kinase panels (≥20 related kinases) to determine selectivity index
• Cellular Cytotoxicity: MTT assay on cancer cell lines (e.g., MCF-7, MDA-MB-231) and normal cell lines (e.g., WI-38) to establish therapeutic window [59]

Mechanistic Studies [59]:

• Apoptosis Assay: Annexin V-FITC/propidium iodide staining with flow cytometry
• Cell Cycle Analysis: PI staining and flow cytometry to assess phase-specific arrest
• Migration Inhibition: Wound healing assay to confirm anti-metastatic effects
• Gene Expression: qRT-PCR for apoptotic markers (BAX, Bcl-2, caspases)

Research Reagent Solutions for Selectivity Studies

Table 3: Essential research reagents and tools for VEGFR-2 selectivity investigations

Reagent/Tool	Specific Application	Function in Selectivity Assessment	Examples/References
VEGFR-2 Kinase Assay Kits	In vitro kinase inhibition profiling	Quantify direct enzymatic IC₅₀ and compare with other kinases	Commercial ELISA-based kits [59]
Recombinant Kinase Panels	Selectivity screening	Profile inhibition across kinase family to identify off-target effects	Broad-panel kinase screening services [56]
Molecular Docking Software	Structure-based design	Predict binding modes and interaction energies with VEGFR-2 vs other kinases	Molegro Virtual Docker, AutoDock [21] [10]
MD Simulation Packages	Binding stability assessment	Evaluate complex stability and residence time through nanosecond-scale simulations	GROMACS, AMBER [21] [23]
ADMET Prediction Tools	Drug-likeness and toxicity	Predict pharmacokinetic properties and potential toxicities early in development	QikProp, SwissADME [21] [23]

Emerging Strategies and Future Directions

Dual-Target Inhibition Approaches

Dual-target inhibitors represent a promising strategy to enhance therapeutic efficacy while minimizing resistance [21] [58]. For instance, simultaneous inhibition of VEGFR-2 and K-RAS G12C addresses complementary pathways in tumor progression and angiogenesis [21]. The compound identified as 737734 demonstrated high stability in complex with both targets in molecular dynamics simulations, suggesting potential for enhanced therapeutic outcomes [21].

Advanced Computational Frameworks

Deep learning approaches are accelerating the discovery of selective VEGFR-2 inhibitors [10]. Recent studies utilized RD-Kit and convolutional neural networks to generate 43 million compounds, followed by molecular docking and dynamics simulations to identify novel candidates with strong VEGFR-2 affinity and minimal off-target potential [10]. These AI-driven methods enable rapid exploration of chemical space while incorporating selectivity constraints early in the design process.

Structural Hybridization Techniques

Molecular hybridization of privileged scaffolds continues to yield promising selective inhibitors [51] [59]. For example, quinoxaline derivatives incorporating bis([1,2,4]triazolo)[4,3-a:3′,4′-c]quinoxaline cores demonstrated potent VEGFR-2 inhibition with improved selectivity profiles [59]. These designs leverage strategic incorporation of hydrogen bond donors/acceptors and hydrophobic moieties to optimize interactions with unique VEGFR-2 structural features.

Balancing Computational Efficiency with Predictive Precision in Large-Scale Screening

In the discovery of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) inhibitors, a critical challenge lies in balancing computational efficiency with predictive precision during large-scale virtual screening. VEGFR-2 is a primary regulator of tumor angiogenesis, making it a prominent therapeutic target in oncology [18] [60]. While traditional wet-lab experimentation remains prohibitively expensive and time-consuming for initial screening phases, computational methods have emerged as indispensable tools for rapid candidate identification. However, these methods exist on a spectrum ranging from highly efficient but approximate filtering techniques to exceptionally precise but resource-intensive simulations. This guide provides an objective comparison of current computational methodologies, evaluating their performance characteristics, and offering experimental protocols to assist researchers in selecting optimal strategies for their VEGFR-2 drug discovery pipelines.

Comparative Analysis of Screening Methodologies

Performance Metrics Across Computational Approaches

The table below summarizes the quantitative performance and characteristics of different computational screening methods as applied to VEGFR-2 inhibitor discovery.

Table 1: Performance Comparison of VEGFR-2 Screening Methodologies

Methodology	Screening Scale	Reported Affinity/Performance	Computational Demand	Key Advantages	Documented Limitations
Traditional Virtual Screening [18]	13,313 compounds	Binding Affinity: -11.0 to -11.5 kcal/mol	Moderate (Docking) to High (MD)	Established protocol, good balance for medium libraries	Limited chemical space exploration; scoring function inaccuracies
Deep Learning (FnGATGCN) [48]	Millions from ZINC database	IC~50~: 0.88 µM (VEGFR2); Cell IC~50~: 4.23 µM (A549)	Very High (Model Training)	Ultra-high throughput; integrates graph + fingerprint features	Requires large training datasets; "black box" interpretability issues
Deep Learning (RD-Kit) [10]	43 million generated compounds	Strong affinity per docking validation	Very High (Model Training/Generation)	De novo molecule generation; massive chemical space exploration	Complex validation required; high false positive rate possible
3D Geometric Deep Learning [9]	Not Specified	Favorable properties vs. Axitinib	Very High (3D Structure Learning)	Incorporates spatial molecular geometry	Novel method, less extensively validated
QSAR Modeling [23]	Lead optimization	pIC~50~: 4.208 to 4.698	Low to Moderate	High efficiency for analog screening; interpretable models	Limited to chemical space of training data

Key Experimental Protocols

Traditional Virtual Screening Workflow

A standard protocol for structure-based virtual screening was detailed in a 2023 study discovering novel VEGFR-2 inhibitors from natural product databases [18].

• Database Preparation: A library of 13,313 natural compounds was curated from the African Natural Products Database (ANPDB) and South African Natural Compounds Database (SANCDB). Structures were downloaded in SDF format, protonated, and energy-minimized before conversion to pdbqt format for docking [18].
• Protein Preparation: The VEGFR-2 crystal structure (PDB: 4ASD) was retrieved from the Protein Data Bank. Water molecules, ions, and native ligands were removed. Missing residues were modeled using Chimera's "Build Structure" tool, and polar hydrogen atoms along with Gasteiger charges were added [18].
• Molecular Docking: Docking was performed using AutoDock Vina with an exhaustiveness parameter of 100 for thorough search space exploration. A grid box of 20Å × 20Å × 20Å was centered on the active site. The top 9 poses per compound were generated and analyzed [18].
• Validation: The co-crystalline ligand (Regorafenib) was re-docked to validate the protocol, with the root-mean-square deviation (RMSD) of heavy atoms calculated between the docked and crystallographic poses [18].
• Advanced Simulation: Top hits underwent 100 ns molecular dynamics (MD) simulations using GROMACS to assess complex stability. Binding energies were calculated using the MM-PBSA method [18].

Deep Learning Screening Workflow (FnGATGCN)

A 2024 study established a multimodal deep learning protocol for dual-activity (VEGFR-2 and A549 cell line) prediction [48].

• Data Curation and Preprocessing: Active (IC~50~ < 1 µM) and inactive (IC~50~ > 10 µM) compounds against VEGFR-2 and A549 were sourced from ChEMBL. SMILES sequences were standardized using MolVS, involving structure normalization, desalting, charge neutralization, and duplicate removal [48].
• Model Architecture (FnGATGCN): The framework employs a multimodal feature fusion strategy.
  • Graph Feature Extraction: A Graph Attention and Convolutional Network (GATGCN) processes molecular graphs. Node features (atoms) include atom type, formal charge, and hybridization (44 features total). Edge features (bonds) include bond type and conjugation (14 features total) [48].
  • Fingerprint Feature Extraction: Extended Connectivity Fingerprints (ECFP4) were computed using RDKit to represent molecular substructures [48].
  • Feature Fusion: The graph and fingerprint features are concatenated and passed through fully connected layers for the final activity prediction [48].
• Training: The dataset was split into training, validation, and test sets in an 8:1:1 ratio. The model was trained as a binary classifier using cross-entropy loss [48].
• Virtual Screening and Validation: The trained model screened the ZINC database. Top predictions underwent molecular docking, drug-likeness analysis, and 200 ns MD simulations for validation [48].

The VEGFR-2 Signaling Pathway and Screening Context

Understanding the biological target is crucial for effective screening. The diagram below illustrates the role of VEGFR-2 in angiogenesis, the process targeted by the inhibitors discovered through these computational methods.

This pathway highlights the critical role of VEGFR-2 dimerization and subsequent intracellular signaling in promoting tumor angiogenesis. Computational screening aims to identify small molecules that disrupt this pathway by competitively inhibiting the receptor's kinase activity [10].

Experimental Workflow for Integrated Screening

A robust screening strategy often combines multiple methodologies. The following diagram outlines a comprehensive workflow that integrates both efficient deep learning pre-screening and precise simulation-based validation.

This tiered approach efficiently allocates computational resources, using faster methods to reduce the candidate pool before applying more rigorous and expensive simulations to a select few promising compounds [18] [48].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Successful computational screening relies on specific software tools and databases, the key ones of which are cataloged below.

Table 2: Key Research Reagents and Computational Tools for VEGFR-2 Screening

Tool/Solution Name	Type	Primary Function in Screening	Application Context
AutoDock Vina [18]	Docking Software	Predicts ligand binding modes and affinities	Structure-based virtual screening
GROMACS [18] [36]	MD Simulation Software	Simulates protein-ligand dynamics and stability	Validating binding stability & calculating free energy (MM-PBSA/GBSA)
RDKit [48] [10]	Cheminformatics Toolkit	Handles molecular featurization, fingerprinting, and QSAR	Feature extraction for ML; de novo molecule generation
ChEMBL [48]	Bioactivity Database	Source of curated bioactivity data for model training	Building training sets for QSAR and Deep Learning models
ZINC Database [48]	Compound Library	Provides commercially available compounds for screening	Large-scale virtual screening campaigns
MARTINI Force Field [36]	CG Model	Enables long-timescale MD simulations of membrane proteins	Studying VEGFR-2 TMD dynamics and dimerization

The landscape of computational screening for VEGFR-2 inhibitors offers a range of strategies balancing efficiency and precision. Traditional virtual screening provides a well-validated, balanced approach for medium-sized libraries, while modern deep learning methods enable unprecedented screening scale at the cost of higher computational demand and complexity. The most successful pipelines, as evidenced by recent studies, are not reliant on a single method but instead strategically combine these techniques. Using efficient deep learning or QSAR models as a first-pass filter to reduce massive chemical spaces to a manageable number of candidates, followed by precise molecular docking and rigorous molecular dynamics validation, creates a synergistic workflow that maximizes both computational efficiency and predictive precision in the quest for novel VEGFR-2 inhibitors.

Integrating ADMET and Toxicity Predictions for Early-Stage De-Risking

In anticancer drug discovery, the vascular endothelial growth factor receptor-2 (VEGFR-2) represents a critically important therapeutic target due to its pivotal role in tumor angiogenesis [18] [40]. Despite significant research investment, the development of effective VEGFR-2 inhibitors faces considerable challenges, with poor pharmacokinetic profiles and toxicity issues accounting for a substantial proportion of clinical failures [40]. The integration of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions early in the drug discovery pipeline has emerged as a powerful strategy for de-risking candidate compounds before substantial resources are committed to synthesis and biological evaluation [61].

The predictive accuracy of ADMET profiling has been significantly enhanced through the development of comprehensive benchmark datasets and advanced computational models [61]. These tools enable researchers to prioritize compounds with optimal pharmacokinetic and safety profiles, particularly for challenging targets like VEGFR-2 where selectivity and toxicity concerns are paramount [62]. This comparative analysis examines current methodologies, experimental protocols, and performance metrics in ADMET integration for VEGFR-2 inhibitor development, providing researchers with a framework for early-stage de-risking strategies.

Computational ADMET Profiling: Methodologies and Workflows

Fundamental Workflow for Early-Stage ADMET Integration

The computational workflow for ADMET integration in VEGFR-2 inhibitor discovery follows a structured pathway that combines predictive modeling with experimental validation. Figure 1 illustrates this multi-stage process, which encompasses initial compound screening, detailed pharmacokinetic assessment, and toxicity prediction.

Figure 1. Computational workflow for integrating ADMET predictions in VEGFR-2 inhibitor discovery. The process begins with drug-likeness screening and progresses through binding affinity assessment, comprehensive ADMET profiling, and toxicity prediction before advancing to resource-intensive molecular dynamics simulations and experimental validation.

Key ADMET Properties and Prediction Methodologies

Table 1: Essential ADMET Properties for VEGFR-2 Inhibitor Profiling

ADMET Category	Specific Properties	Prediction Methods	Optimal Range for VEGFR-2 Inhibitors
Absorption	Aqueous solubility (KSOL), Human intestinal absorption, Caco-2 permeability	In vitro assays, QSAR models, Machine learning	Solubility > 50 μM, High intestinal absorption [63] [61]
Distribution	Blood-brain barrier penetration, Plasma protein binding, Tissue distribution	PBPK modeling, In silico prediction	Low BBB penetration preferred, Moderate to high plasma protein binding [35] [62]
Metabolism	Cytochrome P450 inhibition (CYP2D6, CYP3A4), Hepatic stability (HLM, MLM)	Molecular docking, Metabolic site prediction	Non-CYP2D6 inhibition, Low hepatic clearance [40] [61]
Excretion	Renal clearance, Biliary excretion	Physiologically-based modeling	Balanced renal/hepatic excretion
Toxicity	Genotoxicity, Carcinogenicity, Hepatotoxicity, Cardiotoxicity	In vitro assays, Structural alert screening	Non-mutagenic, Non-carcinogenic, Low hepatotoxicity [40] [62]

Experimental Protocols for ADMET Validation

In Vitro ADMET Assay Protocols

The transition from computational predictions to experimental validation requires standardized protocols for key ADMET properties. The following methodologies represent current best practices in the field:

Microsomal Stability Assay (HLM/MLM)

• Purpose: Evaluate metabolic stability in human liver microsomes (HLM) and mouse liver microsomes (MLM) [63]
• Protocol: Incubate test compound (1 μM) with liver microsomes (0.5 mg/mL) in potassium phosphate buffer (pH 7.4) containing NADPH regenerating system at 37°C
• Sampling: Collect aliquots at 0, 5, 15, 30, and 60 minutes
• Analysis: Terminate reaction with cold acetonitrile, analyze by LC-MS/MS
• Data Interpretation: Calculate intrinsic clearance (CLint) with values < 10 μL/min/mg considered low clearance [63]

Solubility Determination (KSOL)

• Purpose: Measure kinetic solubility in aqueous buffer [63]
• Protocol: Prepare compound stock solution in DMSO, dilute to 100 μM in phosphate buffer (pH 7.4)
• Incubation: Shake at 25°C for 24 hours
• Analysis: Filter and quantify concentration by HPLC-UV
• Classification: Compounds with KSOL > 50 μM generally exhibit acceptable solubility [63]

Caco-2 Permeability Assay

• Purpose: Predict human intestinal absorption [61]
• Protocol: Grow Caco-2 cells on transwell inserts for 21 days, confirm monolayer integrity by TEER measurement
• Experiment: Apply compound to apical side, measure appearance in basolateral compartment over 2 hours
• Analysis: Calculate apparent permeability (Papp); Papp > 10 × 10⁻⁶ cm/s indicates high absorption [61]

Computational ADMET Screening Protocol

For virtual screening of VEGFR-2 inhibitors, the following standardized protocol ensures comprehensive ADMET assessment:

• Compound Preparation

  • Generate 3D structures from SMILES notation
  • Perform energy minimization using MMFF94 force field
  • Generate possible tautomers and protonation states at physiological pH

• Drug-Likeness Filtering

  • Apply Lipinski's Rule of Five (MW ≤ 500, LogP ≤ 5, HBD ≤ 5, HBA ≤ 10)
  • Implement Veber rules (rotatable bonds ≤ 10, polar surface area ≤ 140 Ų)
  • Remove compounds with structural alerts or pan-assay interference compounds (PAINS)

• ADMET Prediction

  • Employ platform-specific algorithms (e.g., Discovery Studio, Schrodinger QikProp)
  • Calculate key descriptors: topological polar surface area, LogP, LogD, solubility
  • Predict CYP450 inhibition, hERG liability, and Ames mutagenicity [35] [40]

• Toxicity Profiling

  • Screen for mutagenicity (Ames test prediction)
  • Assess carcinogenicity potential
  • Predict hepatotoxicity and cardiotoxicity risks [40] [62]

Comparative Analysis of VEGFR-2 Inhibitors with Integrated ADMET Profiling

Performance Comparison of Recent VEGFR-2 Inhibitors

Table 2: Experimental Data for VEGFR-2 Inhibitors with ADMET Properties

Compound	VEGFR-2 IC₅₀ (μM)	Cytotoxicity (MCF-7) IC₅₀ (μM)	Solubility	Metabolic Stability	Toxicity Profile	Reference
Nicotinamide-thiadiazol 7a	0.095 ± 0.05	4.64 ± 0.3	High	Stable (HLM)	Non-mutagenic, Non-carcinogenic	[40]
Phthalazine 4b	0.09 ± 0.02	0.06 ± 0.01	Moderate	Stable (HLM)	Low toxicity in VERO cells (IC₅₀ = 3.00-4.75 μM)	[62]
Phthalazine 3e	0.12 ± 0.02	0.06 ± 0.01	Moderate	Stable (HLM)	Low toxicity in VERO cells	[62]
Sorafenib (Reference)	0.051 ± 0.01	6.72 ± 0.5	Low	Moderate	Known hepatotoxicity risk	[40]
African Natural Compound EANPDB 252	Docking: -11.5 kcal/mol	N/A	Predicted favorable	Predicted stable	Recommended for further investigation	[18]

Analysis of Key Findings

The comparative data reveals several significant trends in successful VEGFR-2 inhibitor development:

Potency-ADMET Relationships Recent optimizations have successfully maintained potent VEGFR-2 inhibition while improving ADMET profiles. Compound 7a demonstrates this balance with sub-micromolar VEGFR-2 inhibition (IC₅₀ = 0.095 μM) coupled with favorable solubility and non-mutagenic properties [40]. The phthalazine derivatives 4b and 3e show exceptional cytotoxic potency against MCF-7 cells while maintaining low toxicity in normal VERO cells, indicating promising therapeutic windows [62].

Metabolic Stability Considerations Hepatic stability emerges as a critical differentiator, with successful candidates demonstrating low clearance in human liver microsome assays. The notation "Stable (HLM)" in Table 2 indicates compounds with CLint values < 10 μL/min/mg, a key threshold for metabolic stability [63]. This property directly influences predicted human half-life and dosing frequency.

Toxicity Mitigation Strategies Contemporary design strategies explicitly address historical toxicity issues associated with VEGFR-2 inhibitors like sorafenib. The favorable toxicity profiles of recent candidates (e.g., non-mutagenic and non-carcinogenic predictions for compound 7a) result from targeted molecular design that eliminates structural alerts while maintaining potency [40].

VEGFR-2 Signaling and Inhibitor Mechanism

VEGFR-2 Signaling Pathway and Therapeutic Intervention

The strategic importance of VEGFR-2 in angiogenesis and the mechanism of action for small molecule inhibitors are illustrated in Figure 2, which details the signaling cascade and intervention points.

Figure 2. VEGFR-2 signaling pathway in tumor angiogenesis and inhibitor mechanism. VEGF binding induces receptor dimerization and autophosphorylation, activating downstream pathways that promote endothelial cell proliferation, survival, and angiogenesis. Small molecule inhibitors competitively bind the ATP-binding site, blocking kinase activity and subsequent signaling cascades.

Essential Research Toolkit for VEGFR-2 Inhibitor Development

Table 3: Essential Research Tools for VEGFR-2 Inhibitor Discovery

Resource Category	Specific Tool/Assay	Application in VEGFR-2 Research	Key Features
Computational Databases	ChemDiv Database [35]	Source of compounds for virtual screening	>1.28 million commercially available compounds
	African Natural Products Database [18]	Source of natural product derivatives	13,313 unique African natural compounds
	PharmaBench [61]	ADMET benchmark dataset	52,482 entries across 11 ADMET properties
Experimental Assays	VEGFR-2 Kinase Inhibition Assay [40]	Direct measurement of target engagement	Fluorescence-based or ELISA format
	MCF-7 Cytotoxicity Assay [40] [62]	Evaluation of anti-proliferative activity	Estrogen receptor-positive breast cancer cell line
	MDA-MB-231 Cytotoxicity Assay [40]	Evaluation of anti-proliferative activity	Triple-negative breast cancer cell line
	HLM/MLM Stability Assay [63]	Metabolic stability assessment	Human and mouse liver microsomes
Software Tools	Molecular Docking (AutoDock Vina) [18]	Binding pose prediction	Open-source, grid-based docking
	Molecular Dynamics (GROMACS/AMBER) [18] [40]	Binding stability assessment	100-200 ns simulation timescales
	MM/PBSA or MM/GBSA [18]	Binding free energy calculation	Post-processing of MD trajectories

The integration of computational ADMET and toxicity predictions represents a transformative approach to early-stage de-risking in VEGFR-2 inhibitor development. The comparative data presented demonstrates that contemporary drug discovery campaigns successfully leverage these methodologies to identify compounds with optimized therapeutic profiles—balancing potent VEGFR-2 inhibition against favorable pharmacokinetic and safety properties. The standardized protocols, benchmarking datasets, and research tools detailed in this analysis provide a framework for systematic candidate optimization. As ADMET prediction platforms continue to evolve through machine learning and larger training datasets, their impact on reducing attrition rates in oncology drug development is expected to grow significantly. For research teams targeting VEGFR-2 and other kinase targets, the integration of these computational approaches at the earliest stages of design represents a strategic imperative for efficient resource utilization and increased probability of clinical success.

Benchmarking Predictive Models Against Experimental Results

Comparative Analysis of Computational vs. Experimental IC₅₀ Values

In the field of drug discovery, particularly in the development of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) inhibitors for cancer therapy, the half-maximal inhibitory concentration (IC₅₀) serves as a critical quantitative measure of compound potency [64] [65]. This value represents the concentration of a drug required to inhibit a biological process or response by 50% in experimental settings, with lower values indicating higher potency [65]. The emergence of sophisticated computational approaches has introduced powerful in silico tools for predicting IC₅₀ values, creating a paradigm where virtual screening can precede and guide experimental validation [66] [22].

This comparative guide objectively analyzes the correlation and discrepancies between computational predictions and experimental measurements of IC₅₀ values within VEGFR-2 inhibitor research. The evaluation is framed within the broader thesis of binding affinity prediction, addressing a critical need in targeted cancer therapy development where VEGFR-2 plays a pivotal role in tumor angiogenesis [67] [21]. As the pharmaceutical industry increasingly relies on computational methods to accelerate drug discovery, understanding the relationship between predicted and empirical potency values becomes essential for researchers, scientists, and drug development professionals seeking to optimize their investigative workflows.

Theoretical Foundations: IC₅₀ in Context

The IC₅₀ value is frequently utilized in conjunction with other pharmacological metrics, each providing distinct information about compound activity. Understanding these distinctions is crucial for accurate interpretation of drug potency data:

• IC₅₀ measures functional inhibition, representing the concentration needed to block a biological process by half [65]. In VEGFR-2 research, this typically refers to inhibition of kinase activity or cancer cell proliferation [68] [69].

• Kd (dissociation constant) quantifies binding affinity between the drug and its target, with lower values indicating tighter binding [65]. While related to IC₅₀, these metrics capture different aspects of molecular interaction.

• EC₅₀ measures activation potency, indicating the concentration required to elicit a 50% response for agonists [65].

A critical consideration in IC₅₀ determination involves experimental model systems. Traditional two-dimensional (2D) monolayer cell cultures often yield different IC₅₀ values compared to three-dimensional (3D) spheroid models, with the latter potentially providing more physiologically relevant data for in vivo extrapolation [64]. This model-dependent variability highlights the importance of standardized experimental protocols when comparing potency values across different studies.

Computational Methodologies for IC₅₀ Prediction

Structure-Based Approaches

Structure-based drug design utilizes the three-dimensional architecture of the target protein to predict inhibitor binding and efficacy:

• Molecular Docking: This technique predicts the orientation and binding affinity of small molecules within the target's active site. Studies targeting VEGFR-2 employ docking simulations to evaluate compound interactions with key residues like GLU885, ASP1046, CYS919, and GLU917 [66] [68]. The binding energy scores (expressed in kcal/mol) serve as indicators of potential inhibitory potency, with more negative values suggesting stronger binding and potentially lower IC₅₀ [22].

• Molecular Dynamics (MD) Simulations: MD simulations assess the stability of protein-ligand complexes over time, typically spanning 100-200 nanoseconds in contemporary studies [66] [68]. These simulations evaluate conformational changes, root mean square deviation (RMSD), and binding mode stability, which correlate with functional inhibition [22].

• Binding Free Energy Calculations: Methods like MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) and MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) provide quantitative estimates of binding affinity. For promising VEGFR-2 inhibitors, these calculations have yielded binding free energies ranging from -32.5 to -34.7 kcal/mol [68].

Ligand-Based and Hybrid Approaches

• Quantitative Structure-Activity Relationship (QSAR) Modeling: QSAR models correlate molecular descriptors with biological activity using statistical methods. Recent QSAR studies for VEGFR-2 inhibitors have reported strong predictive power with R² values up to 0.929 [23].

• Hybrid Virtual Screening: Combined approaches integrate multiple computational techniques. One hierarchical protocol successfully screened 40,000 compounds through sequential ADME filtering, ligand-based prediction, and structure-based docking to identify dual VEGFR-2/K-RAS inhibitors [21].

Table 1: Computational Methods for IC₅₀ Prediction in VEGFR-2 Inhibitor Research

Method Type	Specific Technique	Key Output Parameters	Typical Timeline	Resource Requirements
Structure-Based	Molecular Docking	Binding affinity (kcal/mol), Interaction patterns	Hours to days	Moderate computational power
Structure-Based	MD Simulations	RMSD, RMSF, Binding stability	Days to weeks	High-performance computing
Structure-Based	MM/GBSA/PBSA	Binding free energy (ΔG, kcal/mol)	Days	High-performance computing
Ligand-Based	QSAR Modeling	pIC₅₀, Molecular descriptors	Hours to days	Moderate computational power
Hybrid	Virtual Screening Workflows	Hit compounds, Prioritized libraries	Days to weeks	Variable depending on scope

Diagram 1: Comprehensive computational workflow for IC₅₀ prediction of VEGFR-2 inhibitors, integrating multiple in silico validation steps.

Experimental Methodologies for IC₅₀ Determination

Biochemical and Cellular Assays

Experimental determination of IC₅₀ values employs standardized laboratory protocols to quantify compound potency:

• In Vitro Kinase Assays: These biochemical assays directly measure VEGFR-2 enzymatic inhibition using recombinant proteins. The recently developed thiadiazole-based compound 11a demonstrated an IC₅₀ of 0.056 ± 0.005 µM against VEGFR-2 kinase, comparable to sorafenib (0.045 ± 0.006 µM) [68]. These assays typically employ ELISA-based detection methods to quantify phosphorylated substrates.

• Cell-Based Viability Assays: Cellular models assess compound effects on proliferation using established cancer cell lines. The MTT or SRB assays are commonly employed after 48-72 hours of drug exposure [68] [69]. For instance, benzothiazole derivative 6b exhibited potent cytotoxicity with IC₅₀ values of 0.21 µM against VEGFR-2 in cellular models [69].

• Cell Cycle and Apoptosis Analysis: Mechanistic studies complement IC₅₀ determination through flow cytometry to assess cell cycle arrest and apoptosis induction. Promising VEGFR-2 inhibitors like compound 11a have demonstrated significant G2/M phase arrest (66.48% in HepG-2 cells) and late-stage apoptosis induction [68].

Advanced Model Systems

Advanced experimental models provide enhanced physiological relevance:

• 3D Spheroid Cultures: Compared to traditional 2D monolayers, 3D spheroids mimic the limited drug penetration observed in solid tumors, potentially yielding more clinically predictive IC₅₀ values [64]. Computational models indicate that drug diffusivity and mechanism of action significantly influence the IC₅₀ differences between 2D and 3D systems [64].

• In Vivo Efficacy Models: While not directly measuring IC₅₀, animal studies provide critical context for in vitro potency data. For example, compound 11a showed no significant changes in liver and kidney function biomarkers at 20 mg/kg in murine models, supporting its therapeutic potential [68].

Table 2: Experimental Protocols for IC₅₀ Determination of VEGFR-2 Inhibitors

Assay Type	Key Steps	Endpoint Measurements	Typical Duration	Cell Lines/Models
In Vitro Kinase Assay	1. Incubate VEGFR-2 with ATP/substrate\n2. Add inhibitor compounds\n3. Detect phosphorylation	Phosphorylation signal reduction\nIC₅₀ calculated from dose-response	2-4 hours	Recombinant VEGFR-2 protein
Cellular Viability (MTT/SRB)	1. Seed cells in 96-well plates\n2. Treat with compound dilution series\n3. Incubate 48-72 hours\n4. Add reagent and measure	Absorbance (MTT) or dye binding (SRB)\n% Viability vs. concentration	3-4 days	MCF-7, HepG-2, A-498
Cell Cycle Analysis	1. Compound treatment\n2. Fixation and permeabilization\n3. Propidium iodide staining\n4. Flow cytometry	DNA content histogram\n% Cells in G0/G1, S, G2/M phases	2-3 days	HepG-2, A-498
Apoptosis Assay	1. Compound treatment\n2. Annexin V/PI staining\n3. Flow cytometry	Early/late apoptosis percentages\nCaspase activation	2-3 days	HepG-2, A-498

Diagram 2: Standardized experimental workflow for IC₅₀ determination of VEGFR-2 inhibitors using cell-based assays.

Comparative Analysis: Computational Predictions vs. Experimental Data

Quantitative Correlation Assessment

Direct comparison of computational predictions and experimental measurements reveals both correlations and discrepancies:

Table 3: Computational Predictions vs. Experimental IC₅₀ Values for Selected VEGFR-2 Inhibitors

Compound	Computational Prediction	Experimental IC₅₀	Experimental Model	Variance
Sorafenib (Reference)	Docking: -10.2 kcal/mol [22]	0.045 µM (kinase) [68]	In vitro kinase assay	Reference standard
Thiadiazole 11a	MM/GBSA: -34.7 kcal/mol [68]	0.056 µM (kinase) [68]	In vitro kinase assay	24% less potent
Benzothiazole 6b	Docking: -11.5 kcal/mol [69]	0.21 µM (kinase) [69]	In vitro kinase assay	78% less potent
Cynaroside (VT-6)	Docking: -14.6 kcal/mol [66]	Cell-based: 12.86 µM (MCF-7) [66]	Cellular viability assay	Significant drop in cellular context
Benzothiazole 5b	Not specified	4.26 µM (A-498) [69]	Cellular viability assay	Reference for cellular activity

The data demonstrates that computational binding energy calculations generally show good correlation with biochemical kinase assays but exhibit more substantial variance when compared to cellular viability assays. This discrepancy highlights the additional complexity of cellular systems, where factors like membrane permeability, metabolic stability, and off-target effects influence the observed IC₅₀ [64] [69].

Strengths and Limitations Analysis

Computational Approaches - Strengths:

• High-throughput screening capability (thousands of compounds rapidly) [21] [22]
• Lower resource requirements compared to experimental methods
• Provide structural insights for compound optimization
• Ability to predict binding mechanisms and key interactions

Computational Approaches - Limitations:

• Variable accuracy in predicting cellular IC₅₀ values [69]
• Limited incorporation of pharmacokinetic parameters
• Dependence on protein structure quality and force field accuracy
• Underestimation of cellular context complexity [64]

Experimental Approaches - Strengths:

• Direct measurement of biological activity in relevant systems
• Capture complex cellular responses and off-target effects
• Provide mechanistic data (apoptosis, cell cycle effects)
• Established translation to in vivo models

Experimental Approaches - Limitations:

• Lower throughput and higher resource requirements
• Inter-laboratory variability in protocol execution
• Model-dependent results (2D vs. 3D cultures) [64]
• Limited structural guidance for compound optimization

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Key Research Reagent Solutions for VEGFR-2 IC₅₀ Studies

Reagent/Material	Function in IC₅₀ Determination	Example Specifications
Recombinant VEGFR-2 Kinase	Target protein for biochemical inhibition assays	Human, catalytic domain, >90% purity [68]
Cancer Cell Lines	Cellular models for viability and proliferation assays	MCF-7 (breast), HepG-2 (liver), A-498 (kidney) [68] [69]
MTT/SRB Reagents	Cell viability detection in proliferation assays	MTT: 5mg/mL in PBS; SRB: 0.4% in 1% acetic acid [69]
VEGFR-2 Antibodies	Detection of protein expression and phosphorylation	Phospho-specific (Tyr951, Tyr1175) for signaling studies [67]
Reference Inhibitors	Benchmark compounds for assay validation	Sorafenib, Sunitinib (≥98% purity) [68] [69]
Apoptosis/Cell Cycle Kits	Mechanistic studies of compound action	Annexin V-FITC/PI kits, DNA content detection reagents [68]
Molecular Docking Software	Computational prediction of binding affinity	AutoDock Vina, Schrödinger Suite [22]
MD Simulation Packages	Assessment of binding stability and dynamics	GROMACS, AMBER, NAMD [66] [68]

This comparative analysis demonstrates that computational and experimental approaches to IC₅₀ determination provide complementary rather than redundant information in VEGFR-2 inhibitor development. Computational methods excel in rapid screening and structural guidance but show limitations in predicting cellular activity, while experimental assays deliver biological relevance at the cost of throughput and resources.

The optimal research strategy employs an integrated approach where computational predictions prioritize compounds for experimental validation, creating an efficient iterative design-make-test-analyze cycle. As computational methods continue to evolve with improved force fields, machine learning integration, and better accounting for cellular complexity, the correlation between predicted and empirical IC₅₀ values will likely strengthen, further accelerating the development of potent VEGFR-2 inhibitors for cancer therapy.

For researchers in this field, the key recommendation is to employ computational predictions as valuable prioritization tools while maintaining rigorous experimental validation across multiple model systems, including both biochemical and cellular assays. This balanced approach maximizes the strengths of both methodologies while mitigating their individual limitations, ultimately supporting more efficient and effective drug discovery efforts.

The discovery of novel inhibitor scaffolds is a critical endeavor in modern drug discovery, particularly for challenging targets like vascular endothelial growth factor receptor-2 (VEGFR-2), which plays a pivotal role in tumor angiogenesis [53] [70]. The integration of in silico methods with traditional in vitro validation has created a powerful paradigm for identifying and optimizing new therapeutic compounds with improved efficiency and reduced costs [71] [23]. This guide objectively compares the performance of various recently discovered inhibitor scaffolds for VEGFR-2, providing supporting experimental data and detailing the methodologies that led to their successful identification and validation.

Success Stories in Novel Scaffold Discovery

Furo/Thienopyrimidine Scaffolds

Rationale and Design: Researchers designed novel furo[2,3-d]pyrimidine and thieno[2,3-d]pyrimidine derivatives as type II VEGFR-2 inhibitors based on structure-activity relationship studies of known inhibitors [20]. These designs incorporated three essential pharmacophoric features: a flat heteroaromatic ring system to target the ATP-binding region, a hydrogen bond donor-acceptor pair (amide or urea moiety) for interaction with Glu885 and Asp1046 in the DFG domain, and a terminal aromatic ring with various lipophilic substituents to occupy the allosteric hydrophobic pocket revealed in the DFG-out conformation [20].

Experimental Validation: The synthesized compounds underwent rigorous in vitro testing against VEGFR-2 kinase enzyme. Seven compounds demonstrated highly potent dose-related inhibition with IC₅₀ values in the nanomolar range [20]. Particularly impressive were the thieno[2,3-d]pyrimidine derivatives 21b, 21c, and 21e, which exhibited IC₅₀ values of 33.4, 47.0, and 21 nM, respectively [20]. The furo[2,3-d]pyrimidine-based derivative 15b showed 99.5% inhibition of human umbilical vein endothelial cell proliferation at 10 μM concentration [20]. In in vivo studies using an Ehrlich ascites carcinoma solid tumor murine model, compounds 21b and 21e, when administered orally at 5 and 10 mg/kg/day for 8 consecutive days, demonstrated potent anticancer activity by reducing VEGFR-2 phosphorylation and inducing apoptosis without obvious toxicity [20].

Benzo-Fused Heteronuclear Compounds

Scaffold Diversity: Research groups have extensively explored benzo-fused heteronuclear compounds including benzimidazole, benzoxazole, and benzothiazole derivatives as VEGFR-2 inhibitors [70]. These scaffolds were investigated based on information about the active site of VEGFR-2 and pharmacophoric features of FDA-approved drugs [70]. The design strategy typically maintains the essential binding interactions while exploring novel chemical space to overcome limitations of existing inhibitors.

QSAR and Computational Optimization: In a recent study, researchers built a reliable QSAR model with strong statistical parameters (R² = 0.929, R²adj = 0.917, Q²cv = 0.899) to identify novel benzoxazole/benzimidazole-based VEGFR-2 inhibitors [23]. Virtual screening identified compound 35 (pIC₅₀ = 4.165) as a lead molecule, which facilitated the design of five novel candidates with higher predicted pIC₅₀ values ranging from 4.208 to 4.698 [23]. These designs were achieved through strategic addition and substitution of electron-rich groups (-OH, -NH₂, -F) at key positions. Docking simulations with VEGFR-2 revealed excellent binding affinities, and molecular dynamics simulations combined with MM-GBSA analysis showed that the designed compound 35d had a better propensity for stabilizing VEGFR-2 than the reference drug sorafenib [23].

Drug Repurposing for VEGFR-2 Inhibition

Computational Screening Approach: Researchers have employed computational methods to discover new uses for existing FDA-approved drugs, significantly reducing development time and costs [72]. Through a combination of two different docking methods, molecular dynamics simulations, and quantum-chemical calculations, five existing drugs were identified as potential VEGFR-2 inhibitors from the Drugbank library [72].

Validated Hits: The identified compounds included vilazodone (a psychiatric drug), pranlukast and zafirlukast (asthma drugs), antrafenine (an analgesic and anti-inflammatory drug), and iloperidone (a psychiatric drug) [72]. These five compounds exhibited more stable interactions with VEGFR-2 than sorafenib, with zafirlukast showing the most stable binding affinity [72]. The binding poses of pranlukast and vilazodone were similar to sorafenib, while antrafenine and zafirlukast interacted differently, particularly in the orientation of their ring structures [72]. These compounds inhibited VEGFR-2 by interacting with key residues Cys919, Glu885, and Asp1046, with Lys868 and Phe1047 playing important roles in stabilizing the interaction conformations [72].

Table 1: Comparison of Novel VEGFR-2 Inhibitor Scaffolds

Scaffold Type	Representative Compounds	VEGFR-2 IC₅₀ Values	Cellular Activity	In Vivo Efficacy
Thieno[2,3-d]pyrimidine	21b, 21c, 21e	33.4, 47.0, 21 nM	Potent anti-proliferative effects on HUVECs	Tumor growth inhibition at 5-10 mg/kg/day in EAC murine model
Furo[2,3-d]pyrimidine	15b	Not specified	99.5% HUVEC proliferation inhibition at 10 μM	Not specified
Benzo-fused heteronuclear compounds	Compound 35 derivatives	pIC₅₀: 4.208-4.698 (predicted)	Not specified	Not specified
Repurposed drugs	Zafirlukast	More stable binding than sorafenib (computational)	Anti-tumor effects reported for some compounds	Not specified

Experimental Protocols and Methodologies

In Silico Screening Workflow

The successful identification of novel inhibitor scaffolds typically follows a comprehensive computational workflow:

Molecular Docking: Most studies employ multiple docking programs to ensure reliability. Common approaches include using AutoDock Vina and Sybyl with docking spheres defined around the active site (typically 12 Å radius) [71] [72]. The crystal structure of VEGFR-2 (commonly PDB codes: 4ASD, 1YWN, 4AGD, 2OH4) is prepared by removing water molecules and adding hydrogen atoms before docking simulations [72] [70].

Molecular Dynamics Simulations: Successful studies typically run MD simulations for 50-200 ns using packages like AMBER or GROMACS to evaluate the stability of protein-ligand complexes [72] [23]. Systems are solvated in water boxes with periodic boundary conditions, neutralized with counterions, and energy-minimized before production runs [23].

Binding Free Energy Calculations: The Molecular Mechanics/Generalized Born Surface Area method is widely used to calculate binding free energies. This approach combines molecular mechanics energy terms with continuum solvation models to provide quantitative estimates of binding affinities [23].

QSAR Modeling: Building robust QSAR models involves calculating molecular descriptors, selecting relevant features, and applying machine learning algorithms. Recent studies have successfully employed eXtreme Gradient Boosting and other ML approaches to predict biological activity [71] [23].

Figure 1: Integrated Computational-Experimental Workflow for Novel Inhibitor Discovery

In Vitro Validation Protocols

Kinase Inhibition Assays: The standard protocol involves testing compounds against recombinant VEGFR-2 kinase enzyme using assays that measure phosphorylation of specific substrates [20]. Initial single-dose screening at 10 μM concentration is typically followed by dose-response studies to determine IC₅₀ values [73] [20]. assays often include reference inhibitors like sorafenib or sunitinib for comparison [20].

Cellular Proliferation Assays: Anti-proliferative effects are evaluated on human umbilical vein endothelial cells and various cancer cell lines using methods like MTT or WST-1 assays [20]. Compounds are typically tested across a concentration range (0.01-100 μM) with incubation periods of 48-72 hours [20].

Additional Cellular Assays: Successful studies often include apoptosis assays (Annexin V staining), cell cycle analysis by flow cytometry, and Western blotting to confirm effects on VEGFR-2 phosphorylation and downstream signaling pathways [71] [20].

In Vivo Evaluation Methods

Animal Models: The Ehrlich ascites carcinoma solid tumor murine model is commonly used for evaluating anti-tumor and anti-angiogenic effects [20]. Compounds are typically administered orally at doses of 5-10 mg/kg/day for 8-14 consecutive days [20].

Assessment Metrics: Tumor volume measurements, immunohistochemical analysis of microvessel density (CD31 staining), and Miles vascular permeability assays are standard methods for evaluating anti-angiogenic efficacy in vivo [20]. Toxicity is assessed through body weight monitoring, behavioral observations, and histological examination of major organs [20].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 2: Key Research Reagents and Solutions for VEGFR-2 Inhibitor Development

Reagent/Solution	Function/Application	Examples/Specifications
Recombinant VEGFR-2 kinase enzyme	In vitro kinase inhibition assays	Available from commercial suppliers (e.g., BPS Bioscience); used with specific substrates [20]
HUVEC (Human Umbilical Vein Endothelial Cells)	Anti-proliferative activity assessment	Model system for evaluating anti-angiogenic effects; typically used in MTT/WST-1 assays [20]
Cancer cell lines	Cellular activity screening	Includes HeLa, A549, MDA-MB-231, MCF-7, HepG2; tested across concentration ranges [74] [20]
VEGFR-2 crystal structures	Molecular docking studies	PDB codes: 4ASD, 1YWN, 4AGD, 2OH4; prepared by removing water, adding hydrogens [70]
Molecular dynamics software	Simulation of protein-ligand interactions	AMBER, GROMACS; typically 50-200 ns simulations with explicit solvation [72] [23]
Animal tumor models	In vivo efficacy evaluation	Ehrlich ascites carcinoma solid tumor model; compound administration at 5-10 mg/kg/day [20]

Structural Insights and Binding Patterns

The structural analysis of successful VEGFR-2 inhibitors reveals conserved binding patterns and key interactions:

ATP-Binding Region: The flat heteroaromatic ring systems of inhibitors form crucial hydrogen bonds with the backbone NH of Cys919 in the hinge region [70] [20]. This interaction is essential for anchoring the inhibitor in the ATP-binding pocket.

DFG Domain Interactions: The hydrogen bond donor-acceptor pairs (typically amide or urea moieties) interact with Glu885 and Asp1046 residues in the DFG domain [70] [20]. The NH motifs often form two hydrogen bonds with Glu885, while the CO motifs bond with Asp1046.

Allosteric Pocket Occupation: The terminal aryl moieties occupy the hydrophobic allosteric pocket created by the DFG-out conformation, forming extensive van der Waals interactions [70] [20]. Sufficient space around this aromatic ring allows for various substituents that modulate potency and selectivity.

Type II Inhibitor Advantages: Compounds designed to stabilize the DFG-out conformation typically show improved kinase selectivity and slower off-rates compared to type I inhibitors that target the active kinase conformation [20].

Figure 2: Essential Structural Features of Successful VEGFR-2 Inhibitors

The integrated application of in silico and in vitro methods has proven highly successful in identifying novel VEGFR-2 inhibitor scaffolds with promising therapeutic potential. The comparison of different scaffold types reveals distinct advantages: furo/thienopyrimidine derivatives demonstrate exceptional potency in both enzymatic and cellular assays with confirmed in vivo efficacy; benzo-fused heteronuclear compounds offer extensive opportunities for structural optimization through QSAR-guided design; and repurposed FDA-approved drugs provide accelerated paths to clinical application. The consistent identification of compounds with nanomolar potency across these diverse scaffolds validates the computational-experimental workflow and provides researchers with multiple starting points for further development. As computational methods continue to advance, particularly in machine learning and free energy calculations, the efficiency and success rate of novel scaffold discovery is expected to further improve, addressing the critical need for new therapeutic options in anti-angiogenic cancer therapy.

Evaluating the Performance of Different Scoring Functions and Algorithms

The discovery of novel Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) inhibitors represents a critical frontier in anti-angiogenic cancer therapy. VEGFR-2, a receptor tyrosine kinase, is a primary mediator of tumor angiogenesis, facilitating the growth of new blood vessels that supply nutrients to cancerous tissues [11] [75]. The evaluation of potential drug candidates increasingly relies on computational methods to predict binding affinity between small molecules and the VEGFR-2 target protein. This guide provides a comprehensive comparison of the scoring functions and algorithms that drive these predictions, offering researchers in drug development a framework for selecting appropriate computational tools in their VEGFR-2 inhibitor discovery pipelines.

Performance Comparison of Scoring Approaches

Quantitative Comparison of Scoring Methodologies

The table below summarizes the performance metrics of various scoring functions and algorithms as applied to VEGFR-2 inhibitor prediction:

Table 1: Performance Metrics of Different Scoring Approaches for VEGFR-2 Inhibition Prediction

Scoring Method	Algorithm/Software	Performance Metrics	Key Advantages	Limitations
Molecular Docking	AutoDock Vina [18]	Binding affinity scores: -11.5 to -11.0 kcal/mol for top hits	Fast screening of large compound libraries; Detailed interaction analysis	Limited receptor flexibility in standard implementations
QSAR Modeling	CatBoost on RDK7 fingerprints [76]	R²: 0.792±0.075 (internal), 0.859 (external)	Excellent predictive accuracy for bioactivity (pIC50)	Requires substantial training data with activity measurements
Deep Learning (Generative)	Junction Tree VAE [76]	Reconstruction accuracy: 0.7048; Novelty: 0.9671	De novo design of novel molecular structures	Complex implementation; High computational resources
3D Deep Learning	Geometric-Enhanced ML (GEM-GNN) [9]	Identified candidates with properties superior to Axitinib	Incorporates 3D structural information directly	Requires accurate 3D structural data for training
Machine Learning Ensemble	K-Nearest Neighbors (KNN) [77]	Prediction accuracy: 82.4% (training), 80.1% (test)	Simple, interpretable model for classification tasks	Limited to binary classification (inhibitor/non-inhibitor)

Specialized Application Performance

For specific research applications, specialized scoring approaches have demonstrated distinct advantages:

Table 2: Specialized Applications of Scoring Functions in VEGFR-2 Research

Research Context	Optimal Method	Key Findings	Experimental Validation
Natural Product Screening	Molecular Docking + MM-PBSA [18] [32]	Identified novel inhibitors from African natural compound databases	100ns molecular dynamics simulations confirmed stability
Multi-Target Inhibition	Shape-Based Deep Learning [10]	Generated inhibitors targeting VEGFR-1, VEGFR-2, and VEGFR-3 simultaneously	Molecular dynamics assessed conformational stability
Selectivity Profiling	Machine Learning + Topomer CoMFA [77]	Predicted inhibitors with potentially fewer side effects	Molecular docking confirmed strong hydrogen bond interactions

Experimental Protocols for Key Methodologies

Molecular Docking with AutoDock Vina

Protocol Objective: To perform virtual screening of compound libraries against VEGFR-2 through molecular docking.

Detailed Methodology:

• Protein Preparation: Retrieve VEGFR-2 crystal structure (e.g., PDB ID: 4ASD). Remove water molecules, ions, and native ligands. Add hydrogen atoms and assign charges using tools like MGLTools or Chimera [18].
• Ligand Preparation: Obtain compound structures in SDF format. Convert to PDBQT format using python scripts (e.g., mk_prepare_ligand.py in MGLTools). Apply energy minimization and protonation at physiological pH [18].
• Grid Box Configuration: Set grid center coordinates to -24.611 Å, -0.388 Å, -10.929 Å with lattice size of 20 Å × 20 Å × 20 Å and spacing of 0.375 Å to encompass the binding site [18].
• Docking Parameters: Use exhaustiveness value of 100 for thorough search space exploration. Generate 9 poses per ligand. Employ the Lamarckian genetic algorithm for conformational sampling [18].
• Validation: Re-dock known crystal structure ligands and calculate RMSD of heavy atoms to validate docking protocol accuracy [18].

QSAR Model Development with Machine Learning

Protocol Objective: To build predictive Quantitative Structure-Activity Relationship models for VEGFR-2 inhibition.

Detailed Methodology:

• Data Curation: Collect compounds with known VEGFR-2 inhibitory activity (IC50 values). Preprocess structures: standardize tautomers, remove duplicates, apply variance threshold of 0.05 to descriptors [76] [77].
• Feature Engineering: Generate molecular descriptors and fingerprints. RDKit fingerprints with maxPath=7 (RDK7) have shown superior performance. Apply Maximum Relevance Minimum Redundancy (MRMR) feature selection to reduce dimensionality [76] [77].
• Model Training: Implement multiple algorithms (CatBoost, Random Forest, SVM, etc.) with k-fold cross-validation. For CatBoost, achieve optimal performance with R² of 0.792±0.075 through hyperparameter tuning [76].
• Model Validation: Evaluate on external test set using coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE) as metrics [76].
• Application: Use trained model to predict pIC50 values of novel compounds as part of virtual screening pipelines [76].

Deep Learning-Based Molecular Generation

Protocol Objective: To generate novel VEGFR-2 inhibitor candidates using deep learning approaches.

Detailed Methodology:

• Model Architecture: Implement Junction Tree Variational Autoencoder (JTVAE) to learn continuous molecular representation in latent space. Train on MOSES dataset or similar large compound libraries [76].
• Latent Space Optimization: Apply two optimization strategies: Bayesian Optimization (BO) with Gaussian processes for extensive latent space exploration, and Gradient Ascent (GA) for localized optimization around known active compounds [76].
• Compound Generation: Decode optimized latent vectors to generate novel molecular structures as SMILES strings. Apply MOSES filters to ensure chemical validity and uniqueness [76].
• Evaluation: Assess generated compounds through QSAR prediction, molecular docking, and binding mode analysis with key residues (Cys919, Asp1046, Glu885) [76].
• Validation: Subject top candidates to molecular dynamics simulations to confirm binding stability and calculate binding free energies using MM-PBSA [76].

Visualizing VEGFR-2 Research Workflows

VEGFR-2 Signaling Pathway and Drug Targeting

Diagram 1: VEGFR-2 signaling and inhibition. VEGF binding to VEGFR-2 triggers dimerization, autophosphorylation, and activation of downstream pathways promoting angiogenesis. Inhibitors block this signaling cascade, potentially suppressing tumor growth [11] [78].

Computational Drug Discovery Workflow

Diagram 2: Computational workflow for VEGFR-2 inhibitor discovery. The process integrates multiple scoring approaches from initial screening through lead optimization and validation, leveraging the complementary strengths of different algorithms [18] [76] [32].

Research Reagent Solutions for VEGFR-2 Studies

Table 3: Essential Research Reagents and Computational Tools for VEGFR-2 Inhibitor Discovery

Resource Category	Specific Tools/Databases	Primary Function	Application in VEGFR-2 Research
Protein Structure Resources	PDB IDs: 4ASD, 4AGC, 2OH4 [18] [32]	Source of 3D VEGFR-2 structures for docking	Provides crystallographic coordinates for binding site definition
Compound Libraries	African Natural Products Database (ANPDB) [18]	Repository of natural product compounds	Source of novel, diverse chemical scaffolds for screening
Docking Software	AutoDock Vina [18]	Molecular docking and virtual screening	Calculates binding affinity and pose prediction
Molecular Dynamics	GROMACS, AMBER [18]	Simulates protein-ligand dynamics	Assesses complex stability and refines binding energies
QSAR Modeling	RDKit [76] [77]	Generates molecular descriptors and fingerprints	Builds predictive models for inhibitor activity
Deep Learning Frameworks	Junction Tree VAE [76]	Generative molecular design	Creates novel inhibitor candidates optimized for VEGFR-2
ADMET Prediction	FAF4drug [32]	Predicts pharmacokinetic properties	Filters compounds for drug-likeness and toxicity

The landscape of scoring functions and algorithms for VEGFR-2 inhibitor discovery reveals a diverse ecosystem of complementary approaches. Molecular docking excels at rapid screening and providing structural insights, while QSAR models offer superior predictive accuracy for bioactivity when sufficient training data exists. Emerging deep learning methods show remarkable potential for generative molecular design but require substantial computational resources and expertise. The most effective strategies for VEGFR-2 inhibitor development increasingly integrate multiple approaches, leveraging docking for initial screening, QSAR for activity prediction, and molecular dynamics for validation. As these computational methods continue to evolve, they promise to accelerate the discovery of novel, potent, and selective VEGFR-2 inhibitors for anti-angiogenic cancer therapy.

Establishing Best Practices for a Combined Computational-Experimental Workflow

The vascular endothelial growth factor receptor 2 (VEGFR-2) is widely recognized as a critical molecular target for antiangiogenic cancer therapy. As a tyrosine kinase receptor, VEGFR-2 mediates endothelial cell proliferation, migration, and survival upon binding with VEGF ligands, facilitating the tumor angiogenesis essential for cancer growth and metastasis [53]. The overexpression of VEGFR-2 across diverse cancer types—including breast, non-small cell lung, colorectal, hepatocellular, and cervical carcinomas—correlates with poor patient prognosis, solidifying its therapeutic significance [79] [10]. Although several small-molecule VEGFR-2 inhibitors (e.g., sorafenib, sunitinib, regorafenib) have received FDA approval, their clinical utility is often constrained by side effects and acquired drug resistance [79] [80] [53]. These limitations underscore the necessity for continued discovery of novel inhibitors, a process increasingly reliant on integrated computational-experimental pipelines to enhance efficiency and success rates.

Comparative Analysis of Computational Methodologies and Performance

Virtual Screening and Molecular Docking Approaches

Virtual screening represents the foundational computational step for identifying potential VEGFR-2 inhibitors. Current methodologies employ both structure-based (SBVS) and ligand-based (LBVS) virtual screening, each with distinct advantages. SBVS utilizes the three-dimensional structure of the protein target to dock and score small molecules, while LBVS leverages known active compounds to identify new candidates through similarity metrics [81].

A 2023 study screening 13,313 African natural compounds against VEGFR-2 (PDB ID: 4ASD) demonstrated rigorous protocol implementation. Researchers used AutoDock Vina with an exhaustiveness parameter of 100 and a grid box spacing of 0.375 Å centered at (−24.611 Å, −0.388 Å, −10.929 Å). This approach identified four compounds with binding affinities ranging from -11.0 to -11.5 kcal/mol, comparable to the reference drug Regorafenib [18]. Another investigation utilizing the ICM-PRO software for docking 500,000 ZINC database compounds against VEGFR-2 (PDB ID: 4ASE) identified 53 promising candidates based on docking scores superior to the reference inhibitor tivozanib [80].

Table 1: Key Parameters for Molecular Docking in VEGFR-2 inhibitor Discovery

Parameter	Typical Settings	Purpose	Reference Study
Software	AutoDock Vina, ICM-PRO, Molegro Virtual Docker	Pose generation and scoring	[18] [80] [10]
Exhaustiveness	100 (vs. default of 8)	Enhances reproducibility of docking results	[18]
Grid Box Center	Target-dependent (e.g., -24.611, -0.388, -10.929 for 4ASD)	Defines binding site location	[18]
Grid Box Size	20 Å × 20 Å × 20 Å	Ensures comprehensive coverage of binding site	[18]
Scoring Functions	PLP, MolDock, piece-wise linear potential	Ranking ligand binding affinity	[10]

Machine Learning and Deep Learning Advancements

Artificial intelligence has dramatically accelerated VEGFR-2 inhibitor discovery, overcoming limitations of traditional virtual screening. A 2024 study developed a Fingerprint-enhanced Graph Attention Convolutional Network (FnGATGCN) model that integrated molecular graph features with fingerprint descriptors to predict compounds with dual activity against VEGFR-2 and A549 lung cancer cells. This multimodal fusion approach demonstrated high accuracy and stability, successfully identifying novel active compounds from the ZINC database [13].

Another innovative application utilized RD-Kit and convolutional neural networks (CNNs) to generate 43 million novel compounds targeting VEGFR-1, VEGFR-2, and VEGFR-3. The models were trained on established inhibitors using H-bond donor, H-bond acceptor, molecular weight, and LogP values according to Lipinski's Rule of Five. Molecular docking validation identified several high-affinity candidates, including PubChem IDs 71465,645 and 11152946, demonstrating the powerful generative capacity of deep learning approaches [10].

Critical Considerations in Data Handling and Model Validation

A paramount challenge in computational affinity prediction is ensuring model generalizability beyond training datasets. Recent research has revealed significant train-test data leakage between the widely used PDBbind database and the Comparative Assessment of Scoring Function (CASF) benchmarks. This leakage artificially inflates performance metrics, with nearly 49% of CASF complexes having highly similar counterparts in the training set [82].

The proposed PDBbind CleanSplit addresses this issue through structure-based filtering that eliminates data leakage and reduces dataset redundancies. When top-performing models like GenScore and Pafnucy were retrained on CleanSplit, their performance dropped substantially, indicating previous benchmarks had overestimated true generalization capabilities. In contrast, the Graph neural network for Efficient Molecular Scoring (GEMS) maintained robust performance when trained on CleanSplit, demonstrating genuine generalization to independent test complexes [82].

Experimental Validation and Benchmarking Protocols

Molecular Dynamics Simulations and Free Energy Calculations

Molecular dynamics (MD) simulations provide critical insights into the stability and binding mechanisms of protein-ligand complexes under dynamic conditions. Standard protocols involve solvating the docked VEGFR-2-ligand complex in explicit water models (e.g., TIP3P) with physiological ion concentrations (150 mM NaCl). Simulations are typically performed using AMBER or similar software with the ff14SB force field for the protein and GAFF for ligands [18] [80].

A representative protocol for VEGFR-2 inhibitors includes:

• System Preparation: Solvation in TIP3P water model with Na+/Cl− ions at 150 mM concentration
• Equilibration: 5 ns for minimization and system equilibration
• Production Run: 100-200 ns of conventional MD simulation at 310.15 K
• Analysis: Trajectory analysis for root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and hydrogen bonding patterns [18] [80] [23]

The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) or Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methods are subsequently employed to calculate binding free energies. Typically, 250 snapshots are collected from the last 20-50 ns of the trajectory with equal intervals for these calculations [18] [80].

Experimental Biological Assays

Computational predictions require rigorous experimental validation to confirm efficacy. Standard experimental workflows include:

In Vitro Kinase Inhibition Assays: These measure direct inhibition of VEGFR-2 kinase activity. Promising compounds from a 2022 study showed IC50 values ranging from 0.19 to 0.60 μM compared to 0.08 μM for sorafenib, with compound 11 exhibiting particularly potent inhibition (IC50 = 0.19 μM) [79].

Cellular Proliferation Assays: Anti-proliferative effects are evaluated across cancer cell lines (e.g., A549 lung cancer, HepG-2 hepatoma, Caco-2 colon cancer, MDA-MB-231 breast cancer). The same study reported IC50 values from 6.48 to 38.58 μM for novel piperazinylquinoxaline derivatives, outperforming sorafenib across multiple cell lines [79].

Apoptosis and Mechanism Studies: Flow cytometry analysis of apoptosis induction and Western blotting for key markers (e.g., BAX/Bcl-2 ratio, caspase-3, p53) provide mechanistic insights. Compound 11 increased apoptosis in HepG-2 cells from 5% to 44% and significantly upregulated pro-apoptotic markers [79].

Table 2: Experimental Validation Results for Representative VEGFR-2 Inhibitors

Compound	VEGFR-2 IC50 (μM)	Cellular Anti-proliferative IC50 (μM)	Key Assays	Reference
Sorafenib	0.08	Varies by cell line	Reference standard	[79]
Compound 11	0.19	9.52-12.45	Kinase assay, Cell proliferation, Apoptosis, Western blot	[79]
Piperazinylquinoxaline derivatives	0.19-0.60	6.48-38.58	Kinase assay, Cell proliferation	[79]
Z-3	0.88	4.23 (A549)	Kinase assay, Cell proliferation, MD simulations	[13]
Natural Compounds (EANPDB 252, etc.)	N/A	N/A	Docking score: -11.0 to -11.5 kcal/mol, MD simulations	[18]

Integrated Workflow and Pathway Visualization

Diagram 1: Combined Computational-Experimental Workflow for VEGFR-2 Inhibitor Discovery. This integrated pipeline begins with target selection and proceeds through sequential computational filtering stages before experimental validation and lead optimization.

Essential Research Reagents and Computational Tools

Table 3: Key Research Reagent Solutions for VEGFR-2 Inhibitor Discovery

Reagent/Resource	Function/Purpose	Example Sources/Software
VEGFR-2 Protein Structure	Structural basis for structure-based drug design	PDB IDs: 4ASD, 4AGC, 4ASE [18] [80]
Compound Libraries	Sources of novel chemical entities for screening	ZINC20, ANPDB, SANCDB [18] [80] [13]
Docking Software	Predicting ligand binding poses and affinity	AutoDock Vina, ICM-PRO, Molegro Virtual Docker [18] [80] [10]
MD Simulation Packages	Assessing complex stability & dynamics	AMBER20, GROMACS [18] [80]
Chemical Descriptor Tools	Generating molecular features for ML	RDKit, CDK [10] [13]
Deep Learning Frameworks	Activity prediction & de novo design	FnGATGCN, RD-Kit, CNN [10] [13]
Kinase Assay Kits	Experimental validation of VEGFR-2 inhibition	Commercial kits measuring phosphorylation [79]
Cancer Cell Lines	Cellular efficacy assessment	A549, HepG-2, Caco-2, MDA-MB-231 [79] [13]

The establishment of robust, integrated computational-experimental workflows represents the forefront of VEGFR-2 inhibitor discovery. Critical to this process is addressing fundamental challenges such as data bias through solutions like PDBbind CleanSplit [82] and leveraging advanced AI methodologies that demonstrate genuine generalizability [10] [13]. The most successful pipelines combine complementary virtual screening approaches, rigorous molecular dynamics validation, and multifaceted experimental testing encompassing both kinase inhibition and cellular efficacy. Future developments will likely focus on enhanced multi-target prediction, improved treatment of receptor flexibility, and more accurate solvation models—all while maintaining strict separation between training and evaluation datasets to ensure predictive reliability. As these methodologies continue to mature, they promise to accelerate the discovery of novel, effective VEGFR-2 inhibitors with improved safety profiles and reduced susceptibility to resistance mechanisms.

Conclusion

The comparative analysis of binding affinity prediction methods for VEGFR-2 inhibitors underscores a powerful synergy between foundational computational techniques and cutting-edge artificial intelligence. While molecular docking and dynamics simulations provide crucial mechanistic insights, deep learning models offer unprecedented speed and pattern recognition for vast chemical spaces. The future of VEGFR-2 drug discovery lies in the integrated application of these multi-faceted computational approaches, rigorously validated by experimental biology, to efficiently navigate the challenges of resistance and selectivity. This will accelerate the development of next-generation, dual-target inhibitors and compounds with favorable pharmacokinetic profiles, ultimately translating into more effective and safer anti-angiogenic therapies for cancer patients.

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