Integrated machine learning and molecular dynamics-driven multi-target virtual screening of FDA-approved drugs for drug repurposing in breast cancer. (PubMed, In Silico Pharmacol)
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...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)...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. The online version contains supplementary material available at 10.1007/s40203-026-00681-w.