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Updated: Jan 10, 2026
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
Exploring the Therapeutic Potential of Virtual Screened Novel HER2 Inhibitors via QSAR, Molecular Docking and
Khurram Rehman1, Zoya Iqbal2, Zhiqin Deng3
1Faculty of Pharmacy, Gomal University, D.I.Khan, KPK, Pakistan.
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.
Insights
Computational methods identified a promising HER2 inhibitor, compound 2048788, for advanced breast cancer treatment. This discovery advances drug discovery and optimization for targeted therapies.
Area of Science:
- Computational chemistry and drug discovery
- Oncology and molecular biology
Background:
- HER2 overexpression is a hallmark of advanced breast cancer, correlating with poor prognosis.
- Targeting HER2 is crucial for developing effective breast cancer therapies.
Purpose of the Study:
- To discover and analyze potential HER2 inhibitors using computational approaches.
- To advance drug discovery and optimization for HER2-targeted therapies.
Main Methods:
- Ligand-based virtual screening (LBVS) of the ChEMBL database.
- Molecular docking and molecular dynamics (MD) simulations to assess binding affinity and stability.
- Quantitative structure-activity relationship (QSAR) modeling to identify key structural features.
Main Results:
- Five lead compounds were identified, with compound 2048788 showing superior binding affinity (docking score -11.0 kcal/mol) and predicted pIC50 ≈ 8.6.
- MD simulations confirmed the stability of the identified compound-HER2 complexes.
- An improved QSAR model highlighted hydrogen bond donor count, lipophilicity, and sp3 carbon fraction as key positive contributors to inhibitory activity.
Conclusions:
- Compound 2048788 emerged as a highly promising HER2 inhibitor through integrated computational methods.
- These findings support further preclinical development of novel, targeted HER2 therapies for breast cancer.
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