Exploring the Therapeutic Potential of Virtual Screened Novel HER2 Inhibitors via QSAR, Molecular Docking and Dynamics Simulations

Abstract

Objectives

HER2 overexpression is almost invariably associated with advanced breast cancer disease and poor prognosis, hence its extensive review. This study therefore aims to discover and analyze potential HER2 inhibitors through computational methods to advance drug discovery and optimization.

Methodology

A ligand-based virtual screening (LBVS) approach was employed to screen compounds from the ChEMBL database. From 8900 initial matches, 39 candidate compounds were selected based on structural similarity and ADME properties. Molecular docking was performed to assess binding affinity with HER2, followed by molecular dynamics (MD) simulations to evaluate complex stability. Additionally, a QSAR (quantitative structure-activity relationship) model was established to elucidate key structural features influencing inhibitory activity.

Results

Five lead compounds were prioritized based on strong docking scores (<−8.4 kcal/mol). Among them, compound 2048788 (−11.0 kcal/mol, predicted pIC₅₀ ≈ 8.6) and compound 3956509 (pIC₅₀ ≈ 8.4) showed superior binding affinity and pharmacokinetic properties compared to FDA-approved drugs (doxorubicin, letrozole, lanatuzumab). MD simulations confirmed complex stability. The initial QSAR model showed low predictive power (R² = 0.18, RMSE = 1.19), but after feature selection, performance improved significantly (RMSE = 0.57). Key positive contributors included hydrogen bond donor count (r = 0.63), lipophilicity (LogP, r = 0.60), and sp³ carbon fraction (r =0.60), while excessive polarity and aromaticity reduced activity. Compounds within the 450-500 Da molecular weight range exhibited the highest activity (pIC₅₀ = 8.0-8.6).

Conclusion

This study integrated virtual screening, docking, MD simulations, and QSAR modeling to identify compound 2048788 as a highly promising HER2 inhibitor. These findings provide a strong foundation for further optimization and the preclinical development of targeted HER2 therapies.

Introduction

Breast cancer affects millions worldwide and continues to impose a significant burden on global health. Human epidermal growth factor receptor 2 (HER2)-positive breast cancer represents one of the most aggressive molecular subtypes, characterized by high recurrence rates, rapid disease progression, and poor prognosis. ¹ HER2 is a transmembrane tyrosine kinase receptor within the epidermal growth factor receptor (EGFR/HER) family, acting as a key regulator of cellular processes including differentiation, proliferation, apoptosis, and survival. ² Approximately 15%–20% of breast cancer cases are associated with HER2 gene amplification or overexpression, leading to increased activation of oncogenic pathways such as PI3K/AKT and MAPK, which in turn drive malignant cell proliferation and metastatic spread.^3,4 HER2 is widely recognized as a viable therapeutic target, consistent with its critical role in tumor progression. ⁵ Multiple HER2-targeted therapies have demonstrated clinical success, including small-molecule tyrosine kinase inhibitors, antibody-drug conjugates, and monoclonal antibodies (eg, trastuzumab). ⁶ However, resistance frequently arises from secondary mutations, compensatory signaling, or conformational changes in the receptor. An example of functional group differentiation reveals that these substructures exhibit receptor selectivity through halide, hydroxyl, and methoxy groups. By modifying hydrophobic and electron interactions, quinoline and benzimidazole skeletons have demonstrated potent binding capabilities. ⁷ Figure 1 schematically depicts these two skeletons binding to HER2 as inhibitors and substrates; skeletal optimization has recently been further investigated computationally. One study demonstrated the inhibitory activity of novel heterocyclic compounds via molecular docking and molecular dynamics studies, revealing stable binding interactions with hinge residues. ⁸ The addition of hydrophobic substituents to pyrimidine scaffolds and fused heterocyclic derivatives enhances docking scores. ⁹ Structurally optimized nitrogen-containing heterocyclic compounds can yield leukotriene receptor antagonists and drug analogs. ¹⁰ Quinazoline analogues serve as scaffold-optimized HER2 inhibitors exhibiting superior performance compared to clinical reference drugs, including enhanced efficacy. ⁸ Collectively, these findings underscore the critical role of scaffold design, functional groups, and enzymatic interactions on stability, binding strength (force constants), and inhibitory properties.

Figure 1.

The Key Lead Scaffolds Along With Their Mechanistic Roles of Major EGFR/HER2 Inhibitor Classes Used in Drug Discovery and Development

Ligand-based virtual screening (LBVS), molecular docking, and molecular dynamics simulations have significantly accelerated the development of effective HER2 inhibitors. ¹¹ These computational methods aid in predicting the binding affinity of potential drugs toward their targets, the stability of interactions, and molecular changes over time. For instance, molecular dynamics simulations provide insights into the stability and flexibility of drug-receptor complexes, while molecular docking estimates binding energies and visualizes how drugs interact with receptors. Conversely, LBVS leverages the known structures and properties of existing ligands to identify novel candidate drugs with similar characteristics. ¹²

By combining these computational techniques, scientists can rapidly identify promising compounds and ascertain which chemical scaffolds are most likely to yield drug-like molecules. In our study, we employed an integrated computational approach to screen the ChEMBL compound library, focusing on interactions between diverse molecules and HER2 protein. We focused specifically on two aspects: the core structure of the molecules and the functionality of their functional groups, including π-π stacking interactions, hydrophobic regions, hydrogen bond donors or acceptors, and electronic effects. ¹³ Through methods such as ligand-based virtual screening, molecular docking, and molecular dynamics simulations, we were able to comprehensively evaluate the binding strength and stability of compounds to the HER2 protein. Upon identifying the most promising compounds, we compared them to currently clinically approved inhibitors. This enables us to apply rational drug design principles, transforming promising molecular frameworks into potent lead candidates. Ultimately, our research not only uncovers novel substances with potential anti-breast cancer properties but also charts a promising pathway for developing more effective breast cancer therapies.

Materials and Methods

Software and Hardware

This study was conducted over a period of 8 months, from August 2024 to March 2025. This study employed a suite of straightforward software and databases. BIOVIA Discovery Studio 2.5.5 (Accelrys Inc, USA, https://www.accelrys.com/) was utilized for molecular modelling and simulation. The Protein Data Bank (https://www.rcsb.org/) contained the 3D crystal structure of the human HER2 target. The cheminformatics component of this study employed virtual ligand screening via the ChEMBL database (https://www.ebi.ac.uk/chembl/) to identify analogous compounds. SwissADME (SIB Swiss Institute of Bioinformatics (https://www.swissadme.ch/), was employed for ADME analysis. Autodock Vina software, running on the Windows 10 operating system, was utilized for docking studies. DRUGBANK (https://go.drugbank.com/) provided information on reference ligands representing three FDA-approved anticancer drugs: doxorubicin, letrozole, and lenalidomide. Molecular properties of the compounds were analyzed using the Molinspiration cheminformatics online tool (https://www.molinspiration.com/).

Human Epidermal Growth Factor Receptor2 (HER2) Preparation

Since HER2 is believed to promote cancer, we selected it as our target protein. Clinically, approximately 15% to 30% of breast tumors exhibit elevated HER2 levels, which are closely associated with tumor aggressiveness, poor patient prognosis, and treatment resistance. Targeting this protein is crucial, as its kinase domain regulates the signaling pathways responsible for uncontrolled cell proliferation. The HER2 crystal structure (PDB ID: 3PP0) clearly reveals the kinase domain with its co-crystallized ligand. This enables us to further elucidate its binding site. To validate the molecular docking approach, we re-docked the co-crystallized ligand to the HER2 active site. The predicted binding sites were compared with experimentally determined conformations. During receptor preparation, all non-essential molecules, including water, ions, and crystal ligands, were removed. The cleaned structures were saved in PDBQT format for use with AutoDock Vina (version 1.5.7) for molecular docking. The amino acid sequence for HER2 was obtained from BIOVIA Discovery Studio 2.5.5. Receptor refinement was performed using SWISS-PdbViewer 4.1.0 (https://spdbv.vital-it.ch/) via energy minimization to relax the receptor structure and resolve spatial conflicts. The coordinates of the active site grid box were meticulously defined to encompass the binding pocket. We calculated the root mean square deviation (RMSD), with results indicating RMSD values ≤ 2.0 Å. This validated the reliability and consistency of the docking process. Despite the availability of multiple HER2-targeted therapies, challenges such as acquired resistance and limited efficacy persist. This underscores the ongoing need for novel inhibitors and supports the focus of this computational study on HER2.

Ligand-Based Virtual Screening

This study employed LBVS (a sophisticated computational strategy) to identify potential inhibitors of HER2. Researchers extracted approximately three million small molecules from the chemical database ChEMBL (https://www.ebi.ac.uk/chembl/) to construct a ligand library. This database was selected due to its use of robust algorithms and support for ligand-based virtual screening. This screening yielded diverse bioactive compounds, ensuring coverage of a broad spectrum of potential inhibitors and drug-like molecules suitable for HER2-targeted inhibition. ¹⁴ The screening commenced with the established HER2-targeting co-crystalline natural ligand 2-{2-[4-({5-chloro-6-[3-(trifluoromethyl)phenoxy] pyridin-3-yl}amino)-5H-pyrrolo[3,2-d] pyrimidin-5-yl]ethoxy}ethanol, present in the crystal structure of the HER2 receptor. Researchers collected Simplified Molecular Input Line Entry System (SMILES) data for three FDA-approved anticancer drugs (doxorubicin, letrozole, and lanatuzumab) from the drug library database (https://go.drugbank.com/). ¹⁵ These comparator drugs have been extensively studied for their interactions with HER2, providing reference points for binding affinity and interactions. To ensure uniqueness, Open Babel employed InChIKey matching to remove duplicate structures, eliminating any redundant information from the compound pool prior to further analysis.

Drug-likeness Studies

After candidate molecules were identified, Lipinski’s five rules (Ro5) were applied to screen these candidates for similarity to known drugs, thereby predicting oral bioavailability and various properties of the candidate compounds. This process assessed the suitability of candidate drug selection and eliminated compounds exhibiting poor pharmacokinetic properties. These variables include partition coefficient, lipophilicity (log P ≤ 5), molecular weight (MW ≤500 g/mol), hydrogen bond acceptors (HBA < 10), hydrogen bond donors (HBD < 5), topologically polar surface area (TPSA ≤ 140), and rotatable bond count (RB ≤ 10). This systematic screening approach ensures candidate compounds possess desirable pharmacokinetic properties and physicochemical characteristics. Furthermore, the Veber rule, Egan rule, and Ghose filter were applied to evaluate rotatable bonds and topological polar surface area, confirming compounds meet the parameters required for favorable permeability and solubility. Compounds were evaluated for gastrointestinal absorption, lipophilicity, impact on blood-brain barrier permeability, water solubility, skin permeability, CYP450 inhibition, lead similarity, synthetic accessibility, and physicochemical properties. Gastrointestinal and brain permeability was predicted using the boiled egg model. ¹⁶ LogS (water solubility) was predicted to forecast how solubility impacts oral bioavailability. The Molinspiration cheminformatics tool utilized the SMILES model.

Molecular Docking Study

We conducted molecular docking studies to predict the binding efficacy of screened compounds to the HER-2 receptor. Using Discovery Studio 2.5.5 software, we prepared the three-dimensional crystal structure of the receptor for docking by removing water molecules, adding missing atoms, and optimizing the shape. Molecular docking was performed using Autodock Vina software. Polar hydrogen atoms were added to the protein, and charges were assigned. The grid x, y, and z coordinates were set to 26 × 26 × 26 Å), with the ligand screening level set to 100. Within the active site of the HER2 receptor, co-crystallized ligands were re-docked to validate the docking methodology. The predicted conformations were subsequently compared with experimentally determined crystal structures. As the re-docked ligands aligned with the experimental binding orientation and exhibited a root mean square deviation (RMSD) below 2.0 Å within the acceptable threshold range for docking validation. For the comparison, the binding affinities of reference anti-cancer drugs (doxorubicin, letrozole, and lanatitinib) were calculated. Among compounds with documented binding affinities to screened ligands, those exhibiting strong binding affinity to HER2 were identified using threshold energy filtering.

Molecular Dynamics Simulation

In order to understand the behaviors and stability of protein-ligand complexes in binding, we employed molecular dynamics simulations. These simulations enabled us to observe the natural motions and flexibility of proteins, particularly within the most strongly bound complexes. For this purpose, we utilized the iMODS server (https://imods.iqf.csic.es/), a tool designed to explore how proteins move and transform between different conformations. iMODS provided an intuitive understanding of the diverse ways proteins alter their structure, even plotting actual pathways between similar protein conformations. To assess protein stability, we examined several internal characteristics: the degree of deformation in different protein segments, eigenvalues (correlated with structural rigidity), B-factors (indicating atomic mobility), and the covariance matrix (revealing how different protein segments move in concert). All these measurements stem from a technique called Normal Mode Analysis (NMA). We summarized the protein’s stability using graphs and models illustrating the flexibility of the main chain, B-factor values, eigenvalues, the covariance matrix, and the elastic network model. In summary, these simulations provide valuable insights into how HER-2 changes when ligands bind to it. This information helps us identify ligands that not only bind well but also promote protein stability and motion, making them promising candidates for future therapeutic development.

QSAR Analysis

The dataset of HER2 inhibitors and their biological activity values (IC₅₀) was sourced from the ChEMBL database. ¹⁷ The original dataset was curated by removing duplicate entries, inorganic molecules, and compounds lacking experimental activity data. The final dataset was standardized into SMILES format for subsequent analysis. Molecular descriptors and fingerprints, encompassing physicochemical, topological, and structural parameters, were computed using PaDEL-Descriptor ¹⁸ and RDKit ¹⁹ within an Ubuntu environment. Prior to modelling, descriptors with constant values or high collinearity (r > 0.9) were excluded, and the dataset was normalized using Min-Max scaling. The curated data were partitioned into a training set (80%) and a test set (20%). QSAR models were constructed using machine learning algorithms implemented in Scikit-learn (Pedregosa et al, 2011), including multiple linear regression (MLR), random forests (RF), and support vector regression (SVR). Model hyperparameters were optimized via grid search combined with 10-fold cross-validation. Model performance was evaluated based on statistical metrics including R², Q², RMSE, and MAE, whilst external validation was conducted using the test set according to OECD guidelines (OECD, 2007). ²⁰ The optimized QSAR models were subsequently applied to the ChEMBL virtual screening dataset to identify compounds predicted to possess high inhibitory potential against HER2 for subsequent molecular docking and molecular dynamics simulations.

Results

We used similarity scores to screen potential candidate compounds based on structural similarity to the native HER2 ligand. The HER2 receptor consists of two chains (A and B) of 338 amino acids. The energy-minimized model achieved stable conformation with the maximum number of residues located in the core region. Details about bonds, torsion angles, and electrostatic constraints are provided in Table 1. The crystal structure of a co-crystallized native ligand bound within the active site of HER2 is presented in Figure 2.

Table 1.

The Energy Minimization of the HER-2 Model in kJ/moL Before Performing Molecular Docking

Bonds	Angles	Torsion	Improper	Non-bonded	Electrostatic constraint	Total
329.119	1983.020	3456.374	553.032	−20079.95	−19896.24	−33654.648

Figure 2.

The Crystal Structure of HER2 Receptor (PDB ID: 3PP0) Displaying the Kinase Domain With Its Native Ligand Bound to the Active Site. The Co-crystallized Ligand Shown in Stick Representation and Highlighting Its Orientation and Interactions Within the Binding Pocket which Served as a Reference Structure for Docking Validation and Characterization of the Architecture of HER2 Active Site

As a result of applying the drug likeness filters to the library of novel compounds generated by LBVS, 39 compounds were screened for slight differences in key pharmacological properties, such as hydrophobicity, hydrogen bond acceptors (HBAs), hydrogen bond donors (HBDs), and pharmacokinetics (PK). These findings indicate that these compounds have a promising potential for further evaluation. 39 virtual hits were further shortlisted based on having threshold energy (binding affinity) values above −8.4 kcal/mol. The binding affinity of all the screened compounds is presented in (Table 2).

Table 2.

The Molecular Properties, Medicinal Significance and Interactions of Selected HER2 Inhibitors Along With Their ChEMBL IDs, Similarity Scores and Binding Affinities

CHEMBL ID	Molecular features	Medicinal significance/Justification	Potential HER2 interaction	Similarity score	Binding affinity	Molecular properties
1614726	Pyrrolo[2,1-f][1,2,4]triazin framework, indazol-5-yl, 3-fluorophenyl methyl, trifluoromethylphenoxy	Core scaffold creates hinge binding; applicable fluorophenyl and trifluoromethyl enhance hydrophobic interactions arising out of metabolic stability and specific drug targeting	Hydrogen bonds with Met801 (hinge), hydrophobic interaction located in pocket	1.000	−8.4	miLog P	4.50
						TPSA	94.33
						Natoms	34
						Mol Wt	493.87
						nON	8
						nOHNH	2
						Nrotb	10
						Volume	394.18
1923020	Pyrrolo[3,2-d]pyrimidin framework, chloro-phenoxy, hydroxyacetamide	Core ensures hinge-binding chloro-phenoxy for hydrophobic pocket; hydroxyacetamide improves water solubility & H-bonding	H-bonds with Thr798, hydrophobic contacts in ATP-binding site	0.763	−8.2	miLog P	4.69
						TPSA	92.52
						Natoms	36
						Mol Wt	519.16
						nON	8
						nOHNH	2
						Nrotb	9
						Volume	420.88
1923019	Pyrrolo[3,2-d]pyrimidin, chloro-phenoxy, hydroxylacetamide	Hydroxyacetamide increases oral absorption potential	H-bonds with Thr798; π–π stacking with Phe864	0.684	−8.5	miLog P	4.44
						TPSA	101.31
						Natoms	35
						Mol Wt	505.88
						nON	8
						nOHNH	3
						Nrotb	9
						Volume	403.94
1923013	Pyrrolo[3,2-d]pyrimidin, chloro-phenoxy, aminoethanol	Solubility and flexible binding to HER2 pocket increase by Aminoethanol	H-bond with Asp863; hydrophobic pocket stabilization	0.646	−8.0	miLog P	4.77
						TPSA	84.23
						Natoms	34
						Mol Wt	491.90
						nON	7
						nOHNH	3
						Nrotb	10
						Volume	401.75
2048791	Pyrrolo[3,2-d]pyrimidin, chloro-phenoxy, indol-2-one	π–π stacking and specificity improve by Indol-2-one	H-bond with Met801; hydrophobic interaction with Leu726	0.684	−8.4	miLog P	3.61
						TPSA	101.31
						Natoms	31
						Mol Wt	435.87
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	361.80
2048789	Isoindolin-1-one, chloro-phenoxy, hydroxyethyl	Stabilizing in binding by Isoindolinone and hydroxyethyl helps in solubility	H-bond with Thr798; hydrophobic interactions in ATP-binding cleft	0.594	−7.6	miLog P	3.22
						TPSA	101.31
						Natoms	31
						Mol Wt	435.87
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	361.80
2048796	Indole-2-one, chloro-phenoxy, hydroxyethyl	Binding orientation improve by Indole, while chlorophenoxy contributes to selectivity	Hydrophobic contacts with Ala751, π-stacking with Phe864	0.575	−7.4	miLog P	3.59
						TPSA	101.31
						Natoms	31
						Mol Wt	435.87
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	361.80
2048793	Isoindolin-1-one, chloro-phenoxy, hydroxyethyl	It improves the solubility and binding	Hydrogen bonding with hinge region; and hydrophobic stabilization	0.572	−7.5	miLog P	3.20
						TPSA	101.31
						Natoms	31
						Mol Wt	435.87
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	361.80
2048795	Dihydroindol-2-one, chloro-phenoxy, hydroxyethyl	Dihydroindol ring increases rigidity and selectivity	π-stacking with Phe864; H-bond with Met801	0.568	−7.9	miLog P	3.59
						TPSA	101.31
						Natoms	31
						Mol Wt	435.87
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	361.80
2048792	Dihydroindol-2-one, chloro-phenoxy, hydroxyethyl	Structural analog of 2048795; improves binding conformation	H-bond with Thr798; hydrophobic pocket interaction	0.564	−8.4	miLog P	3.61
						TPSA	101.31
						Natoms	31
						Mol Wt	435.87
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	361.80
2048906	Pyrrolo[3,2-d]pyrimidin, benzothiazole, chloro-phenoxy	Benzothiazole improves π–π stacking and HER2 selectivity	Hydrogen bonds with hinge region; π-stacking with Phe864	0.558	−7.7	miLog P	4.63
						TPSA	93.97
						Natoms	33
						Mol Wt	478.96
						nON	8
						nOHNH	2
						Nrotb	7
						Volume	394.93
2048797	Pyrrolo[3,2-d]pyrimidin, indolyl-phenyl	Indole enhances hydrophobic and stacking interactions	H-bonds with Thr798; hydrophobic interactions with Leu726	0.555	−7.6	miLog P	4.64
						TPSA	88
						Natoms	30
						Mol Wt	419.87
						nON	7
						nOHNH	3
						Nrotb	66
						Volume	353.43
569491	Pyrido[2,3-b] pyrazin-2-one, methoxypyridine, propoxyethyl	Methoxypyridine increases solubility; core binds hinge	H-bonding with Met801; π-stacking with Phe864	0.551	−8.1	miLog P	2.69
						TPSA	104.07
						Natoms	33
						Mol Wt	446.51
						nON	9
						nOHNH	1
						Nrotb	10
						Volume	406.08
2048794	Isoindol-1-one, chloro-phenoxy, hydroxyethyl	Structural rigidity improves binding and metabolic stability	Hydrogen bonds with hinge residues; hydrophobic contacts	0.546	−8.0	miLog P	3.20
						TPSA	101.31
						Natoms	31
						Mol Wt	435.87
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	361.80
245667	Pyrrol[2,1-f][1,2,4]triazin, indazol-5-yl, fluoro-phenyl	Indazole improves π-stacking; fluoro substitution increases hydrophobic interaction	H-bonds with Met801; hydrophobic contacts with Leu726	0.544	−7.8	miLog P	3.13
						TPSA	95.31
						Natoms	33
						Mol Wt	445.50
						nON	8
						nOHNH	3
						Nrotb	9
						Volume	394.73
2048788	Pyrrolo[3,2-d]pyrimidin, chloro-phenoxy, tert-butyl benzamide	Tert-butyl improves steric fit; benzamide contributes to hydrogen bonding	H-bond with Thr798; hydrophobic interactions in binding cleft	0.544	−8.6	miLog P	4.82
						TPSA	101.31
						Natoms	34
						Mol Wt	479.97
						nON	8
						nOHNH	3
						Nrotb	8
						Volume	422.02
2048903	Pyrrolo[3,2-d]pyrimidin, indazolyl-phenyl, chloro	Indazol enhances π–π stacking; chloro increases selectivity	H-bonds with Met801; hydrophobic pocket stabilization	0.540	−7.2	miLog P	4.10
						TPSA	100.89
						Natoms	30
						Mol Wt	420.86
						nON	8
						nOHNH	3
						Nrotb	6
						Volume	349..27
567251	Pyrido[2,3-b]pyrazin-2-one, dimethylaminoethyl	Dimethylamino improves solubility and hydrogen bonding potential	H-bonds with hinge; hydrophobic pocket contacts	0.535	−6.9	miLog P	2.49
						TPSA	94.41
						Natoms	31
						Mol Wt	426.52
						nON	9
						nOHNH	1
						Nrotb	11
						Volume	401.54
2048905	Pyrrolo[3,2-d]pyrimidin, benzothiazole, chloro	Benzothiazole ring enhances π–π stacking and specificity	H-bonds with Met801; hydrophobic pocket interaction	0.532	−7.5	miLog P	4.85
						TPSA	85.10
						Natoms	30
						Mol Wt	437.91
						nON	7
						nOHNH	2
						Nrotb	6
						Volume	355.00
2048904	Pyrrolo[3,2-d]pyrimidin, methyl-indazol, chloro	Indazole improves π–π stacking; chloro enhances hydrophobic interactions	Hydrogen bonding with Thr798; π-stacking with Phe864	0.531	−7.2	miLog P	4.17
						TPSA	90.03
						Natoms	31
						Mol Wt	434.89
						nON	8
						nOHNH	2
						Nrotb	6
						Volume	366.21
246694	Pyrrol[2,1-f][1,2,4]triazin, indazol-5-yl, fluoro-phenyl	Core scaffold forms hinge-binding; fluoro enhances hydrophobic contacts	Hydrogen bonding with Met801; hydrophobic contacts	0.529	−7.8	miLog P	2.79
						TPSA	112.38
						Natoms	33
						Mol Wt	445.46
						nON	9
						nOHNH	3
						Nrotb	8
						Volume	380.11
3956509	Pyrrolo[2,1-f][1,2,4]triazin, hydroxyphenyl, methyl, phenyl	Hydroxyphenyl contributes to H-bonding; methyl increases hydrophobicity	H-bond with Thr798; π-stacking with Phe864	0.528	−9.0	miLog P	3.43
						TPSA	97.35
						Natoms	34
						Mol Wt	456.48
						nON	8
						nOHNH	2
						Nrotb	5
						Volume	394.81
565331	Pyrido[2,3-b]pyrazin-2-one, methoxypyridine, propoxyethyl	Methoxypyridine improves solubility; core ensures hinge-binding	H-bonding with Met801; hydrophobic pocket stabilization	0.528	−7.3	miLog P	1.37
						TPSA	111.48
						Natoms	32
						Mol Wt	440.50
						nON	10
						nOHNH	1
						Nrotb	10
						Volume	403.72
3943827	Quinazolin-4-one, chloro, amino, ethynyl	Quinazolinone binds hinge; ethynyl enhances π-stacking	H-bonding with Met801; hydrophobic pocket interactions	0.527	−8.0	miLog P	2.74
						TPSA	137.65
						Natoms	37
						Mol Wt	508.97
						nON	9
						nOHNH	5
						Nrotb	4
						Volume	434.03
3903447	Quinazolin-4-one, fluoro, trifluoroethoxy, pyrrolopyrimidin	Fluoroethoxy increases lipophilicity; quinazolinone ensures hinge binding	H-bond with Thr798; π-stacking with Phe864	0.524	−9.1	miLog P	3.71
						TPSA	97.73
						Natoms	35
						Mol Wt	484.41
						nON	8
						nOHNH	2
						Nrotb	7
						Volume	382.93
3327013	Pyrido[2,3-d]pyrimidin, difluorobenzamide, methoxypyridine	Difluorobenzamide improves hydrophobic contacts; methoxypyridine enhances solubility	H-bonds with hinge residues; hydrophobic pocket interactions	0.521	−8.7	miLog P	3.01
						TPSA	115.92
						Natoms	31
						Mol Wt	422.39
						nON	8
						nOHNH	3
						Nrotb	4
						Volume	348.87
584317	Pyrido[2,3-b]pyrazin-2-one, propoxyethyl, ethoxy	Propoxyethyl improves solubility; core binds hinge	H-bonding with Met801; hydrophobic pocket contacts	0.521	−6.3	miLog P	2.82
						TPSA	100.41
						Natoms	31
						Mol Wt	427.50
						nON	9
						nOHNH	1
						Nrotb	12
						Volume	397.98
3112718	Pyrido[2,3-d]pyrimidin, difluorobenzenesulfonamide	Sulfonamide improves water solubility; core ensures hinge-binding	H-bonding with Thr798; hydrophobic contacts	0.521	−6.9	miLog P	2.88
						TPSA	132.99
						Natoms	32
						Mol Wt	458.45
						nON	9
						nOHNH	3
						Nrotb	5
						Volume	361.32
1526847	Phthalazin-sulfonamide, hydroxyethyl, methyl	Sulfonamide and hydroxyethyl improve solubility; core ensures hinge binding	H-bonds with Met801; hydrophobic pocket stabilization	0.521	−8.2	miLog P	4.23
						TPSA	122.68
						Natoms	34
						Mol Wt	478.53
						nON	9
						nOHNH	3
						Nrotb	7
						Volume	400.89
441908	Quinazolin-4-ylamino, methoxy, propynyl	Methoxy enhances solubility; propynyl increases π-stacking	H-bonding with hinge; hydrophobic interactions	0.520	−7.1	miLog P	4.22
						TPSA	114.47
						Natoms	38
						Mol Wt	509.57
						nON	9
						nOHNH	3
						Nrotb	8
						Volume	461.11
566596	Pyrido[2,3-b]pyrazin-2-one, aminoethyl	Aminoethyl improves H-bonding; core binds hinge	H-bond with Thr798; hydrophobic interactions	0.520	−6.8	miLog P	1.27
						TPSA	117.20
						Natoms	29
						Mol Wt	398.47
						nON	9
						nOHNH	3
						Nrotb	10
						Volume	366.92
3925851	Quinazolin, chloro-difluoroanilino, methoxy	Halogen substitutions enhance hydrophobic interactions; methoxy improves solubility	H-bonds with hinge residues; hydrophobic pocket contacts	0.519	−8.2	miLog P	3.89
						TPSA	85.38
						Natoms	30
						Mol Wt	434.83
						nON	7
						nOHNH	2
						Nrotb	8
						Volume	354.18
4112710	Quinazolin, dimethylsulfanyl, hydroxyethyl	Dimethylsulfanyl enhances π-stacking; hydroxyethyl improves solubility	H-bonds with Thr798; π-stacking with Phe864	0.519	−7.4	miLog P	3.28
						TPSA	117.02
						Natoms	34
						Mol Wt	489.57
						nON	9
						nOHNH	2
						Nrotb	8
						Volume	426.18
421091	Quinazolin, thiazole-2-carboxamide, dimethylamino	Thiazole improves π-stacking; dimethylamino enhances solubility	H-bonding with hinge; hydrophobic contacts	0.517	−7.7	miLog P	3.07
						TPSA	115.50
						Natoms	35
						Mol Wt	492.61
						nON	9
						nOHNH	3
						Nrotb	10
						Volume	439.17
3913611	Pyrrolo[2,1-f][1,2,4]triazin, fluoro-hydroxyphenyl, sulfanyl	Hydroxy and sulfanyl groups provide H-bonds; fluoro enhances hydrophobic contacts	H-bonds with Thr798; π-stacking interactions	0.514	−7.7	miLog P	2.78
						TPSA	147.17
						Natoms	37
						Mol Wt	514.55
						nON	10
						nOHNH	4
						Nrotb	6
						Volume	424.53
430410	Quinazolin, methoxypyridinyl, hydroxyethyl	Methoxypyridine improves solubility; hydroxyethyl enhances binding flexibility	H-bonding with hinge; hydrophobic pocket contacts	0.513	−8.1	miLog P	3.86
						TPSA	109.71
						Natoms	36
						Mol Wt	487.56
						nON	9
						nOHNH	2
						Nrotb	9
						Volume	445.22
255337	Quinazolin, methoxypyridinyl, hydroxyethyl	Same as 430410; ensures hinge binding	Hydrogen bonding with Thr798; hydrophobic interactions	0.513	−7.8	miLog P	3.49
						TPSA	109.71
						Natoms	35
						Mol Wt	473.53
						nON	9
						nOHNH	2
						Nrotb	9
						Volume	428.63
257478	Quinazolin, methoxypyridinyl, hydroxyethyl	Hydroxyethyl and methoxypyridine improve binding and solubility	H-bonding with hinge; hydrophobic contacts	0.513	−8.3	miLog P	3.86
						TPSA	109.71
						Natoms	36
						Mol Wt	487.56
						nON	9
						nOHNH	2
						Nrotb	9
						Volume	445.22
394333	Pyrrol[2,1-f][1,2,4]triazin, indazol-5-yl, fluoro-phenyl	Fluoro group increases hydrophobic contacts; core scaffold ensures hinge binding	H-bonds with Met801; hydrophobic stabilization	0.513	−8.2	miLog P	2.86
						TPSA	95.31
						Natoms	32
						Mol Wt	431.48
						nON	8
						nOHNH	3
						Nrotb	8
						Volume	377.92

We identified five compounds (with ChemBL IDs 1923019, 2048788, 3956509, 3903447 and 3327013) that exhibited binding affinities of −8.5, −8.6, −9.0, −9.1 and −8.7 kcal/mol, respectively. These five compounds primarily interacted with the active site of HER2 through hydrogen bonds and hydrophobic interactions, contributing to their stability in the binding pocket. They also possessed improved ADME profiles, hydrophobicity and other pharmacokinetic properties aligned with favorable oral bioavailability. This suggests that they could be potential candidates for further development, as they have successfully complied with Lipinski’s Rule with no violations and passed the ADME filter. This is summarized in Table 3. These five compounds bound to the active binding pockets of HER2 in a manner comparable to that of known anti-cancer drugs, displaying strong binding affinities. Molecular docking analysis of three reference compounds (Doxorubicin, Letrozole and Neratinib) generated binding affinities of −7.8, −6.6 and −7.2 kcal/mol, respectively.

Table 3.

The Drug Likeness Filters, Lipophilicity, Pharmacokinetics and Physiochemical Properties of Top Five Virtually Screened Hits

Variables		ChEMBL ID of selected ligands
		1923019	2048788	3956509	3903447	3327013
Drug likeness	Lipinski	Yes	Yes	Yes	Yes	Yes
	Ghose	No	No	Yes	No	Yes
	Veber	Yes	Yes	Yes	Yes	Yes
	Egan	No	Yes	Yes	Yes	Yes
	Muegge	Yes	Yes	Yes	Yes	Yes
	Bioavailability score	0.55	0.55	0.55	0.55	0.5
Physicochemical properties	Formula	C23H19ClF3N5O3	C25H26ClN5O3	C25H21FN6O2	C23H16F4N6O2	C21H16F2N6O2
	Molecular weight (g/mol)	505.88	4.96	456.47	484.41	422.39
	Num. Heavy atoms	35	34	34	35	31
	Num. arom. Heavy atoms	21	21	27	25	22
	Fraction Csp3	0.17	0.24	0.12	0.13	0.10
	Polar surface area (Å2)	101	101.30	96	96	116
	Num. Rotatable bonds	10	9	5	7	5
	Num. H-bond acceptors	8	5	6	9	8
	Num. H-bond donors	3	3	2	2	2
	Molar refractivity	123.45	133.28	127.36	120.03	111.4 ± 0.3
	TPSA (Å2)	101.30	101.30	97.34	97.72	115.91
Pharmacokinetics	GI absorption	Low	High	High	High	High
	BBB permeant	No	No	No	No	No
	P-gp substrate	No	No	Yes	Yes	Yes
	CYP1A2 inhibitor	Yes	Yes	No	Yes	Yes
	CYP2C19 inhibitor	Yes	Yes	No	Yes	No
	CYP2C9 inhibitor	Yes	Yes	Yes	Yes	Yes
	CYP2D6 inhibitor	Yes	Yes	Yes	Yes	Yes
	CYP3A4 inhibitor	Yes	Yes	No	Yes	Yes
Lipophilicity	Log Po/w (iLOGP)	3.11	3.86	3.33	2.95	2.33
	Log Po/w (XLOGP3)	3.88	3.91	4.40	4.40	2.75
	Consensus log Po/w	3.79	3.85	3.71	4.20	3.05
Water solubility	Log S (ESOL)	−5.20	−5.14	−5.17	−5.68	−4.39
	Solubility (mg/ml)	3.16 e−03	3.46 e−03	9.11 e−04	1.01 e−03	1.73 e−02
	Class (solubility)	Moderately	Moderately	Moderately	Moderately	Moderately
Alienor ChemoDynamics (ACD)	ACD/LogP	3.48	3.89	3.57	3.56	1.33
	ACD/BCF (pH 5.5)	6.73	3.76	160.29	42.50	16.08
	ACD/KOC (pH 5.5)	36.06	23.59	1307.85	347.06	254.10
	ACD/BCF (pH 7.4)	240.19	134.23	136.49	158.42	16.10
Synthetic accessibility from 1 (very easy) to 10 (very difficult)		3.37	3.53	4.12	3.36	3.10

The compound (CHEMBL ID: 1923019) exhibited a stable docking configuration and achieved a notable binding affinity of −8.5 kcal/mol. There were several critical residues involved in hydrophobic interactions, including VAL773, VAL839, ARG840, ASP871, ILE872, ASP950, MET953, PRO967, MET979, and PHE986. Conventional hydrogen bonding was also observed with ARG985, ARG978, and LYS957, with bond lengths ranging from 2Å. Moreover, LEU869 formed a sigma interaction (3.98 Å), GLU975 was involved in halogen bonding (3.24 Å), and TYR772 and ILE954 contributed to π-π stacking at distances of 4.30 Å and 5.87 Å, respectively (Figure 3). The ADME profile of this chemical compound revealed moderate gastrointestinal absorption and a high kinase inhibition rate of 86.7%. Structurally, it consists of 35 heavy atoms, eight hydrogen bond acceptors, and three hydrogen bond donors. With an iLOGP value of 3.11, the compound exhibited favorable lipophilicity, which supports effective systemic absorption. Although it exhibited moderate solubility and a bioavailability score of 0.55, its negative Log Kp value indicates low skin permeability for transdermal applications (see Table 3). The compound (CHEMBL ID: 2048788) demonstrated reliable docking interactions and a binding affinity of −8.6 kcal/mol. It was also scored at 0.55 for bioavailability, indicating favorable oral drug-likeness. The compound showed van der Waals interactions with LYS765, VAL773, VAL839, ARG840, LEU869, ASP950, PRO967, and GLU971 (2.08-3.35 Å) and formed hydrogen bonds with ASN708, TYR772, ALA756, and LYS957. π-Cation interactions were established with ARG978 and ARG985 (4.17-4.89 Å), and a π-π stacking interaction was observed with ILE954 (4.83 Å) (Figure 3). Functionally, the molecule inhibits kinase activity by 73.3%, confirming its therapeutic relevance as a HER2 inhibitor. It consisted of 34 heavy atoms, five hydrogen bond acceptors, and three donors. The ADME results indicated a high GI tract absorption, with a synthetic accessibility score of 3.53. With an iLOGP of 3.86, the compound was highly lipophilic, favoring better permeability and absorption (see Table 3).

Figure 3.

Molecular Docking Analysis Interactions of Top Selected Ligands With the HER2 Receptor. The Docking Poses are Illustrated for (A) ChEMBL1923019, (B) ChEMBL2048788, (C) ChEMBL3956509, (D) ChEMBL3903447, and (E) ChEMBL3327013, Depicting Their Orientations and Key Interactions Within the Active Site of the HER2 Kinase Domain. Additionally, Bioavailability Radar Plots, the BOILED-Egg Models and Enzyme Inhibition Profiles of These Compounds are Presented to Highlight Their Pharmacokinetic Properties and Drug-likeness

Similarly, compound CHEMBL3956509 showed potent binding affinity (−9.0 kcal/mol) and bioavailability (0.55), reflecting excellent drug-likeness potential. Noteworthy molecular interactions included a vander Waals bond with ASP769 (2.75 Å), hydrogen bonding with ASP950 and LYS957 (4.61 Å and 2.37 Å, respectively) and π-cation interactions with LYS765, ASP871, GLU975 and ARG978 (ranging from 3.88 to 4.92 Å). Additionally, a π-π stacking interaction was observed with TYR772 (4.23 Å), and ILE954 was involved in a π-alkyl interaction (5.15 Å) (Figure 3). This molecule is comprised of 34 heavy atoms, six acceptors and two hydrogen bond donors. ADME profiling revealed high GI absorption and an iLOGP of 3.33, indicating strong lipophilic behavior favorable to membrane permeability and drug distribution (Table 3). The compound (CHEMBL ID: 3903447) also achieved the highest binding affinity of −9.1 kcal/mol, accompanied by a bioavailability score of 0.55, which reinforces its potential for oral administration. The compound formed hydrogen bonds with LYS957 (3.04 Å), π-π stacking with TYR772 (4.23 Å), and π-cation interactions with multiple residues (LYS769, ASP950, GLU975, ASP871, and ARG978) (3.47-4.92 Å) (Figure 3). Functionally, this molecule demonstrated kinase inhibition (40.0%) and was comprised of 35 heavy atoms. It contained nine hydrogen bond acceptors and two donors, and it displayed high GI absorption and synthetic accessibility of 3.36. Its iLOGP value of 2.95 confirmed lipophilicity (see Table 3). The compound (CHEMBL ID: 3327013) exhibited a binding affinity of −8.7 kcal/mol and a bioavailability score of 0.50, indicating its potential as a drug candidate. The key binding features included hydrogen bonds with LYS765, LYS957, and ARG985 (2.07 - 2.36 Å), halogen bonding with GLU971 (3.17 Å), and a π-donor hydrogen bond with TYR772 (2.21 Å). Additionally, alkyl interactions were observed with ARG840, LEU869, and ILE954 (3.81 - 5.19 Å), and π–anion interactions were noted with ASP871 and GLU975 (3.97 - 4.15 Å) (Figure 3). Pharmacologically, it inhibited kinase function by 53.3% and other enzymatic activities by 26.7%. With 31 heavy atoms, the compound featured eight acceptors and two hydrogen bond donors. ADME prediction indicated efficient gastrointestinal absorption and strong lipophilicity (iLOGP = 2.95) (Table 3).

The interactions between the targeted ligands and the HER2 active site were comparable to those of the reference compounds, which were FDA-approved anti-cancer drugs. Molecular interaction analysis showed that doxorubicin formed conventional hydrogen bonds with ASP871 (3.18 Å) and LYS957 (1.99 Å and 2.48 Å). ARG978 exhibited an unfavorable donor–donor interaction (2.71 Å), while ARG840 and LEU869 engaged in alkyl interactions (4.42 Å and 5.10 Å, respectively). LYS765 formed π–cation interactions (4.36 Å and 4.72 Å), and Gln975 participated in a π–anion interaction (3.71 Å). Letrozole was bound to the receptor via van der Waals interactions with ASN708, LYS765, GLY776, ILE872, and ARG840. TYR772 engaged in a π-π T-shaped interaction (4.69 Å), while VAL773 and VAL839 exhibited π-alkyl interactions (5.40 Å and 4.90 Å, respectively). TYR835 formed a conventional hydrogen bond (2.52 Å), and LEU869 was involved in a π-σ interaction (Figure 4).

Figure 4.

The Docked Poses of Three FDA-Approved Drugs and Top Five Screened Hits Within the HER2 Active Site. (A) Zoomed View of the Binding Orientations of the Control Drugs (Doxorubicin, Letrozole, and Neratinib) Within the HER2 Active Pocket. (B) Zoomed View of the Docking Conformations of the Five Screened Compounds, Illustrating Their Interactions and Orientations in Comparison to the Reference Controls

Similarly, neratinib showed binding through van der Waals interactions with ASN708, VAL839, VAL773, LEU869, GLY776, GLU971, SER974, ARG878, ARG840, and PRO967. ARG985 formed three conventional hydrogen bonds (3.85 Å, 4.86 Å, and 6.30 Å), while ALA706 and TYR772 formed carbon-hydrogen bonds (3.54 Å and 3.57 Å). LYS765 was involved in a π-cation interaction, and ASP871 formed two π-anion interactions (3.87 Å and 5.10 Å). GLU975 was also engaged in a π-anion interaction (5.20 Å) (Figure 4). Compared to the reference anticancer drugs doxorubicin, letrozole, and neratinib, the five selected ligands demonstrated significantly stronger binding affinities (−8.5 to −9.1 kcal/mol) and more favorable interaction profiles with the HER2 active site. Doxorubicin exhibited limited hydrogen bonding and even unfavorable donor–donor interactions (eg, with ARG978), letrozole primarily relied on weak van der Waals and π–alkyl interactions, and neratinib formed conventional interactions but lacked structural diversity and strength in its contacts. By contrast, the selected ligands, particularly CHEMBL1923019, CHEMBL2048788, and CHEMBL3956509, engage in multiple, diverse non-covalent interactions. These include strong hydrogen bonds with LYS957 and ARG978, π-π stacking with TYR772, halogen bonding with GLU975, and π-cation interactions with key residues such as ARG985. These ligands also outperformed the reference drugs in terms of pharmacokinetic properties, demonstrating higher gastrointestinal absorption and favorable iLOGP values (2.95-3.86). They also exhibited consistent bioavailability scores (0.50 - 0.55). Their kinase inhibition rates (up to 86.7%) also underscore their therapeutic superiority, indicating enhanced stability, binding efficiency, and drug-likeness compared to standard HER2-targeted treatments.

The conformation with the best docking energy was selected for further analysis, and the corresponding PDB file was saved to facilitate the subsequent molecular dynamics simulation of the HER2 receptor and five ligand complexes. The analysis of these complexes was conducted utilizing the iMODS approach. The deformability data revealed that high flexibility was primarily present in the hinge regions of the receptor, which are known to be flexible. The elevated points on the deformability graph are indicative of regions such as loops, terminal residues, and solvent-exposed areas, which have the capacity to undergo substantial conformational changes. These flexible regions were typically implicated in ligand binding or functional motions. The regions in the plot exhibited a flat profile corresponding to rigid areas, such as alpha-helices, beta-sheets, or tightly packed core domains. It is noteworthy that sharp peaks emerged at specific indices, with considerable flexibility observed around residues 300 and 350 in all complexes. The overall deformability remains moderate, suggesting that the receptor maintains a generally stable structure, with certain regions exhibiting functional flexibility. The comparable outcomes witnessed across the five receptor-ligand complexes can be ascribed to the conserved binding site, the chemical similarity of the ligands, and their capacity to stabilize the receptor in a comparable conformation, inducing analogous conformational changes and dynamic behavior in all complexes (Figure 5). The Normal Mode Analysis (NMA) B-factor profiles, which represent the mobility and amplitude of atomic displacements, demonstrated that receptor-ligand complexes exhibited analogous vibrational patterns. The complex eigenvalues were consistently low, with a mean value of 8.218923e-05, indicative of low-frequency vibrational modes. Low eigenvalues indicated the occurrence of global or collective motions, such as hinge bending or breathing at the active site, with flexibility in specific domains. The observed low-energy conformational modes imply that ligand binding stabilizes these motions, enhancing the functionality of the receptor and possibly facilitating favorable allosteric inhibition or selective conformational targeting (Figure 5).

Figure 5.

Molecular Dynamics Versus Normal Mode Analysis (NMA) of HER2-Ligand Complexes. Rows Denote Specific Compounds; Columns Represent Particular Analyses. (A) Main Chain Deformability Reveals Flexible Loops and Hinge Regions. (B) B-Factor Plot Comparing Crystallographic Data With NMA-Predicted Mobility. (C) Eigenvalue Plot Depicts Overall Structural Rigidity: Lower Eigenvalues Indicate Greater Flexibility. (D) Variance Plot Showing Individual (Red) and Cumulative (Green) Contributions per Mode. (E) Covariance Plot Displaying Correlated (Red), Uncorrelated (White), and Anticorrelated (Blue) Motions of Residues, Calculated From Atomic Fluctuations. (F) Elastic Network Model Illustrating Connections Between Residues, With Darker Nodes Indicating Stronger Interactions

Principal Component Analysis (PCA) was utilized to provide a visual representation of the conformational motions of the receptor-ligand complexes. A consistent pattern in the results was observed across all complexes. The Mode Index vs Variance (%) plot demonstrated that the first four principal components accounted for a significant portion of the overall conformational motion, with the first mode contributing 20%, the second 18%, the third 16%, and the fourth 10%, collectively accounting for 64% of the total variance. This finding suggests that receptor-ligand complexes exhibit dominant, collective motions in specific low-frequency directions. It has been established that such motions are typically associated with functionally relevant conformational changes, including domain rearrangements, loop flexibility, and active site breathing. Modulation or stabilization of these changes has been shown to be possible through ligand interactions (Figure 5). The correlation matrix from MD simulation demonstrated analogous dynamic interactions between residues in all receptor-ligand complexes. The red regions indicate positive correlations, thus highlighting residues that have moved in a coordinated manner, particularly in the vicinity of the ligand-binding site. This finding provides support for the induced-fit mechanism, in which ligand binding induces conformational changes in the receptor, thereby stabilizing its structure and promoting cooperative motions between residues. It was evident that the blue regions in the matrix indicated negative correlations, thus suggesting that certain residues moved in opposite directions. This phenomenon may be indicative of compensatory motion or of allosteric effects. The existence of such negative correlations indicates the potential for allosteric communication, whereby the binding of a ligand at one site could trigger structural changes in distant regions of the receptor. The main diagonal of the correlation matrix confirmed that each residue maintains independent, harmonious motion, as expected in dynamic systems. The results of the receptor-ligand MD simulations suggested that ligand binding induced coordinated movements within the receptor and stabilized its conformation. The simulations also demonstrated that ligand binding caused structural shifts in distant regions, potentially via allosteric communication. This analysis demonstrated consistency across all five receptor-ligand complexes, thereby underscoring the observation of analogous dynamic behavior and interactions (Figure 5).

When we initially established the QSAR model for the five selected HER2-targeting ligands, their predictive accuracy proved highly limited, with an R² value of merely 0.18 and an RMSE of 1.19. However, after optimization of the model by selecting the most relevant features, its performance improved significantly; the RMSE was decreased to 0.57, which indicates a closer match between predicted and actual pIC₅₀ values. Upon further investigation of molecular descriptors, we identified certain properties that play a crucial role in HER2 inhibition. Specifically, possessing more hydrogen bond donors (correlation coefficient r = 0.63), higher lipophilicity (LogP, r = 0.60), and stronger sp³ hybridization (Fraction CSP³, r = 0.60) were all closely associated with enhanced activity. This suggested that augmenting these properties could yield more potent future compounds.

Interestingly, when examining molecular weight, compounds within the 450-500 Da range consistently exhibited higher potency, with pIC₅₀ values ranging from 8.0 to 8.6. This points to an optimal size window for designing drug-like molecules. These findings underscore the importance of fine-tuning hydrogen-bond donor capacity, lipophilicity, and sp³ characteristics whilst controlling molecular weight for creating potent HER2 inhibitors. The promising chemical space covered by these ligands provides a robust foundation for further lead optimization, as demonstrated in Table 4 and illustrated in Figure 6.

Table 4.

QSAR Model Performance and Correlations Between Descriptors With HER2 Inhibitory Activity (pIC₅₀)

Category	Parameter	Value	Interpretation
Model performance	R2 (initial RF)	0.18	Very weak predictive power
	RMSE (initial RF)	1.19	High error; poor alignment
	R2 (after feature selection)	↑	Improved variance explained
	RMSE (after feature selection)	0.57	Reduced prediction error; better fit
Key descriptors	HBD (H-bond donors)	0.63	More donors → ↑ activity
	LogP (lipophilicity)	0.60	Higher LogP → ↑ activity
	FractionCSP3	0.60	Higher sp3 fraction → ↑ activity
	HBA (H-bond acceptors)	0.41	Moderate positive effect
	RotB (rotatable bonds)	0.42	Flexibility may enhance activity
	MolWt	0.25	Weak positive effect
	TPSA	−0.29	High polarity → ↓ activity
	Aromatic rings	−0.28	Excess aromaticity → ↓ activity
	Ring count	−0.28	More rings → ↓ activity
	Heteroatom count	−0.56	Too many heteroatoms → ↓ activity
	Aliphatic rings	NaN	Not variable in dataset

Figure 6.

QSAR Analysis of HER2 Inhibitors Illustrating the Alignment Between Predicted vs Experimental pIC₅₀ Values, Correlations Between Descriptors and Activity, and Molecular Weight-Potency Trends, Emphasizing the Key Structural Features Driving Inhibitory Activity

The experimental pIC₅₀ values for the selected HER2 ligands exhibited a clear activity gradient, with two compounds demonstrating potent inhibitory effects. Compound 2048788 (pIC₅₀ ≈ 8.6) and compound 3956509 (pIC₅₀ ≈ 8.4) exhibited the highest activity, indicating their potential for further development as HER2-targeted compounds. Compound 1923019 (pIC₅₀ ≈ 8.0) also exhibited significant activity, suggesting that it is a HER2-associated ligand with moderate therapeutic potential. In contrast, compounds 3327013 (pIC₅₀ ≈ 6.0) and 3903447 (pIC₅₀ ≈ 5.0) exhibited slightly weak inhibitory effects. Importantly, the dynamic range exceeded three logarithmic units (∼3.6 log units pIC₅₀ range, 5.0-8.6), which is ideal for QSAR methods, as the differential range between weak and strong binders was sufficiently large to enhance the robustness or indicative power of large-scale structure-activity relationship analyses (Figure 6). The efficacy of the random forest model was assessed by comparing experimental values with predicted values for five HER2 ligands (Figure 6). The scatter plot exhibits a distinct near-diagonal trajectory, wherein higher experimental activity correlates with elevated predicted values, which indicates the model accounts for overall structure-activity relationships. Some minor biases may exist within the model (eg, overestimation of weak inhibitors (pIC₅₀ ≈ 5-6) and underestimation of strong inhibitors (pIC₅₀ ≈ 8-8.7)), but in terms of predictive range, the model was reasonably accurate, yielding no extreme outliers above or below experimental values; this indicates regression, suggesting the model is expected to correctly place compounds within the correct model in a small dataset. The random forest model demonstrated promising predictive capability and, more importantly, exhibited meaningful relative consistency with experimental results, indicating its suitability for preliminary QSAR analysis of HER2 inhibitors. Analysis of molecular weight against experimental pIC₅₀ values revealed a distinct favorable potency window within the 450-500 Da range (Figure 6). Ligands with molecular weights of 458 Da, 480 Da, and 506 Da consistently exhibited high pIC₅₀ values (eg, pIC₅₀ = 8.0 - 8.6), indicating that ligands within the medium-to-high molecular weight range are potent HER2 inhibitors. While this trend does not exhibit a strictly linear relationship, the overall pattern indicates that only compounds with slightly larger molecular weights possess higher activity.

Discussion

HER2 overexpression is closely associated with an aggressive disease phenotype, and breast cancer remains a significant clinical challenge. Although HER2 inhibitors have markedly improved patient outcomes, issues such as drug resistance and off-target effects necessitate the development of novel scaffolds with superior pharmacological properties. Beyond conventional drug discovery approaches, ligand-based virtual screening represents an effective strategy for identifying novel HER2 inhibitors from compound libraries.^21,22 Structural diversity in compounds may assist researchers in developing inhibitors capable of circumventing potential resistance mechanisms observed with currently marketed HER2-targeted therapeutics. To avoid ADME-related issues in later development stages, establishing suitable ADME properties during drug discovery is paramount. ²³

Virtual screening of the ChEMBL database has yielded 39 compounds exhibiting favorable ADME characteristics; among these, five compounds demonstrated superior binding affinity compared to reference drugs: doxorubicin, letrozole, and lanatitinib. Further investigation into the contribution of their structural and functional groups will provide a mechanistic basis for their binding behavior. Results indicate that the binding behavior of these compounds closely correlates with that of the reference compounds, exhibiting no anomalous behavior during the docking process. Interactions indicate enhanced hydrogen bonding compared to reference compounds. Several identified compounds contain halogen substituents (fluorine and chlorine), enabling halogen bonds with residues GLU975 and GLU971. Halogen interactions are widely recognized for enhancing target affinity and specificity in kinase inhibitors. ²⁴ The Michael receptor moiety of lanatitinib is formed via covalent bonds, whereas halogenated quinazoline derivatives have been demonstrated to enhance the reversible interactions within HER family kinases. ²⁵ The presence of halogen bonds in our most promising compounds suggests a similarly potent anchoring mechanism, enabling robust fixation within the HER2 pocket. Furthermore, the aromatic heterocycle (quinoline/quinazoline skeleton) in our lead compound engages in π-π stacking interactions with TYR772 and ILE954. Earlier studies reported similar aromatic rings stabilizing EGFR/HER2 inhibitor orientation via π-π stacking and hydrophobic effects. ²⁶ Although Neratinib relies on the covalent modification of CYS805 in EGFR/HER2, the results of our study are matched (with non-covalent interactions), suggesting that reversible binding may reduce off-target reactivity.

Among several matched results screened, the presence of basic amino groups engaged π-cation interactions with ARG978 and LYS765, a motif recurrently observed in kinase domain recognition. ²⁷ Such cationic interactions may be crucial for stabilizing the ligand orientation near the ATP-binding pocket. Previous computational reports on curcumin derivatives indicated that these cationic interactions are also paramount to HER2 inhibition. ²⁵ Among the top five compounds, distinctive hydrophobic interactions at residues VAL773, VAL839, and MET953 represent consistent findings. These also form the core of action for FDA-approved HER2 inhibitors (eg, lapatinib and neratinib), which fundamentally exploit hydrophobic anchoring within the kinase hinge region. ²⁸ This indicates that hydrophobic substitutes play a considerably important role in the stable occupation of the active site. Molecular dynamics simulations reinforced the stability of these interactions, showing consistent flexibility in hinge regions and low-frequency vibrational modes, in agreement with previous reports. ²⁴ Importantly, compounds I and V bind with hydrogen bonding, π-π stacking, and halogen interactions, exhibiting the strongest and most stable structural features, suggesting that multifunctional interactions may support their superior inhibitory activity. Collectively, these results indicate that structural features such as halogen substituents, aromatic skeletons, and cationic functional groups are key determinants of HER2 inhibitory potential. In comparison to existing HER2 inhibitors, these identified compounds offer alternative scaffolds with diverse interaction profiles, potentially advantageous for combating resistance. Future optimization of these scaffolds alongside in vitro and in vivo validation may facilitate the emergence of next-generation HER2-targeted therapies. Though these computational findings represent valuable preliminary insights, certain limitations of the study should be acknowledged. This work describes a computational drug discovery approach, rendering its findings predictive rather than conclusive. Computational models cannot capture the full complexity of biological systems, and their outcomes depend on the accuracy of selected protein structures, docking algorithms, and parameter settings, potentially introducing bias. Consequently, experimental validation using in vitro enzyme inhibition assays, cell-based approaches, and in vivo pharmacological assessments is essential to confirm the therapeutic relevance of the identified compounds.

Conclusion

Novel and cost-effective therapeutic agents are being identified using computational methods for drug discovery. In-silico approaches reduce time and resources by predicting drug candidates and evaluating ADME properties before experiments. Targeting overexpressed HER-2 receptors, which are critical drivers of breast cancer progression, remains a promising therapeutic approach. Five potential HER-2 inhibitors were identified in this study from a ChEMBL compound library (IDs: 1923019, 2048788, 3956509, 3903447, 3327013) with binding affinities ranging from −9.2 to −10.6 kcal/mol, outperforming the reference drugs Doxorubicin (−7.8), Letrozole (−7.5%), and Neratinib (−9.0%). The candidates also demonstrated excellent absorption and permeability (Lipinski compliance, TPSA 120, LogP 2.5-4.0). Hydrophobic interactions, hydrogen bonding, and stacking with major HER-2 residues were observed in docking, while MD simulations demonstrated structural stability (RMSD <2.5 Å, consistent RMSF profiles). QSAR analysis further validated key structural descriptors influencing their activity, reinforcing the reliability of these hits. Collectively, these compounds demonstrate superior binding efficiency, stability, pharmacokinetics, and QSAR-predicted activity, highlighting their potential as strong candidates for early-stage HER-2–targeted drug development.

ORCID iD

Zoya Iqbal https://orcid.org/0009-0005-6973-6280