Machine learning-driven drug repurposing for HER2-positive breast cancer

Breast cancer, particularly the Human Epidermal Growth Factor Receptor 2 (HER2)-positive subtype, remains a significant clinical challenge due to its aggressive nature and the frequent development of resistance to targeted therapy. In this study, we created a comprehensive machine-learning (ML)-based drug repurposing framework to identify new HER2 inhibitors among FDA-approved drugs. A carefully curated dataset of 13,944 HER2 bioactivity records from BindingDB was used to build high-accuracy predictive models. This involved optimizing descriptors, reducing dimensionality, and selecting key features. The Random Forest model trained on 75 optimized descriptors demonstrated the best performance (R² = 0.81, RMSE = 0.86, MAE = 0.6483). This allowed us to understand structure-activity relationships and perform reliable virtual screening of 4,099 FDA-approved drugs. Docking studies against HER2 wild-type (7PCD) revealed strong interactions, with scores from -10.5 to -7.5 kcal/mol. For the exon-20 insertion mutant (8U8X), the affinities were even higher (-11.6 to -8.0 kcal/mol), exceeding the reference co-crystal ligand in both cases. FDA0870 (Timolol maleate) consistently ranked as a top dual inhibitor, forming stable hydrogen bonds and hydrophobic contacts with key catalytic residues of both wild-type and mutant HER2. Molecular dynamics simulations supported its stability, with wild-type HER2-FDA0870 complexes showing an average protein RMSD of 3.4 Å and a ligand RMSD of 2.9 Å, whereas mutant complexes had an average protein RMSD of 3.9 Å and a ligand RMSD of 2.1 Å over extended trajectories. Persistent interactions with residues such as ASP863/ASP867, THR862/THR866, and LYS753 were observed. ADMET analysis indicated that the drug had good drug-like properties, moderate permeability, a high unbound fraction, and acceptable safety features, suggesting its potential for further development. This integrated ML, docking, MD, and ADMET approach demonstrates that FDA0870 is a promising repurposed dual inhibitor for HER2-positive and HER2-mutant breast cancer, highlighting the value of computational pipelines in speeding up the search for targeted cancer therapies.