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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Integrated machine learning and molecular dynamics-driven multi-target virtual screening of FDA-approved drugs for
Kevser Kübra Kırboğa1, Şükran Acar1, Metehan Şen2
1Faculty of Engineering, Department of Bioengineering, Bilecik Şeyh Edebali University, 11100 Bilecik, Turkey.
Abstract:
Breast cancer remains the most frequently diagnosed malignancy among women worldwide, necessitating multi-target therapeutic strategies that address its molecular complexity and resistance mechanisms. This study presents an integrated computational drug repurposing framework combining machine learning (ML)-based pre-screening, molecular docking, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations to identify FDA-approved drugs with polypharmacological potential against five breast cancer targets: HER2, EGFR, VEGFR2, HDAC3, and CDK6. 12 ML algorithms were evaluated per target (AUC-ROC: 0.878-0.951), and SHAP analysis revealed target-specific descriptor patterns. Virtual screening of 3000 FDA-approved drugs identified 94 compounds with pan-inhibitory potential across all four primary targets, from which the top 50 were validated through AutoDock Vina docking (re-docking RMSD < 2.0 Å). Ponatinib emerged as the top-ranked computational candidate (mean: - 10.07 kcal/mol), followed by Regorafenib (- 9.64), Sorafenib (- 9.46), and Entrectinib (- 9.45), while non-oncology drugs including antrafenine, betrixaban, and maraviroc demonstrated novel multi-target binding profiles. All-atom MD simulations (500 ns, CHARMM36m/CGenFF, 310 K) confirmed stable binding poses for five lead complexes, with ligand RMSD values of 1.05-1.85 Å remaining below the 2.0 Å threshold. MM-GBSA calculations revealed a binding hierarchy concordant with docking scores (R2 = 0.92): Ponatinib-VEGFR2 (ΔGbind = - 42.38 kcal/mol) > Entrectinib-CDK6 (- 38.56) > Ponatinib-EGFR (- 33.24) > Entrectinib-HER2 (- 28.47) > Dacomitinib-HDAC3 (- 24.63 kcal/mol). Energy decomposition uncovered target class-dependent thermodynamics, with van der Waals-driven kinase binding versus desolvation-penalized HDAC3 interactions. As the present study is entirely computational, the identified compounds should be regarded as hypothesis generating leads requiring experimental validation through in vitro kinase and HDAC3 inhibition assays, cell based studies, and target engagement confirmation before any translational conclusions can be drawn.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s40203-026-00681-w.
Insights
This study computationally identified FDA-approved drugs for multi-target breast cancer therapy. Ponatinib and other drugs showed promise against key targets like HER2, EGFR, VEGFR2, HDAC3, and CDK6, warranting further experimental validation.
Area of Science:
- Computational drug discovery and repurposing
- Oncology and molecular biology
- Bioinformatics and cheminformatics
Background:
- Breast cancer is a leading global malignancy in women.
- Effective treatment requires strategies targeting multiple molecular pathways and resistance mechanisms.
- Existing therapies face challenges due to tumor complexity and acquired resistance.
Purpose of the Study:
- To identify FDA-approved drugs with polypharmacological potential against key breast cancer targets using a computational framework.
- To evaluate drugs against five targets: HER2, EGFR, VEGFR2, HDAC3, and CDK6.
- To leverage machine learning, molecular docking, and simulations for drug repurposing.
Main Methods:
- Integrated computational framework: machine learning (ML)-based pre-screening, molecular docking, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations.
- Virtual screening of 3000 FDA-approved drugs against five breast cancer targets.
- Validation using AutoDock Vina, MD simulations (500 ns), and MM-GBSA calculations.
Main Results:
- ML models achieved high performance (AUC-ROC: 0.878-0.951).
- 94 compounds showed pan-inhibitory potential; top candidates included Ponatinib, Regorafenib, Sorafenib, and Entrectinib.
- MD and MM-GBSA confirmed stable binding, with Ponatinib-VEGFR2 and Entrectinib-CDK6 showing highest binding affinities.
Conclusions:
- The computational framework successfully identified potential polypharmacological drugs for breast cancer.
- Ponatinib, Regorafenib, Sorafenib, Entrectinib, and Dacomitinib are promising candidates for further investigation.
- Identified compounds require experimental validation for in vitro and in vivo efficacy before clinical translation.
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