Machine learning-driven computational drug repurposing to identify new tubulin inhibitors against cancer

Tubulin is a validated anticancer target, yet the clinical translation of colchicine-binding site inhibitors remains limited by toxicity and resistance. To accelerate the discovery of safer tubulin-targeting agents, we employed a machine learning (ML)-driven drug repurposing strategy integrating computational and experimental validation. Robust AutoQSAR classification models were trained on 279 curated tubulin inhibitors and used to screen 4500 US FDA-approved drugs, predicting 1800 compounds as potential tubulin inhibitors. These candidates were subjected to multistage structure-based virtual screening using Glide HTVS, SP, and XP docking, narrowing the selection from 698 (HTVS) and 350 (SP) to 38 compounds at the XP stage. Binding free-energy calculations (MM-GBSA) and 200 ns molecular dynamics simulations identified four stable colchicine-site binders: omeprazole, podofilox, sulfadoxine, and trimethoprim, exhibiting favourable binding energetics (Glide XP scores -10.06 to -8.12 kcal/mol; ΔG bind ranging from -10.06 to -8.12 kcal/mol; ΔG bind ranging from -64.16 to -38.61 kcal/mol). Biochemical tubulin polymerization assays confirmed tubulin inhibition, while cell-based cytotoxicity studies demonstrated low-micromolar antiproliferative activity of omeprazole and podofilox against melanoma (IC₅₀ = 4.32 ± 0.29 μM and 4.98 ± 0.37 μM, respectively) and colorectal cancer cells (IC₅₀ = 6.22 ± 0.22 μM and 5.76 ± 0.18 μM; n = 3). Overall, this study highlights a ML-guided drug repurposing framework that, unlike prior colchicine binding site-focused virtual screening studies, integrates large-scale ML prioritization with experimental validation to identify novel colchicine-site-targeted anticancer candidates.