A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer
TL;DR
An integrated chemoinformatics and machine learning workflow with ligand-based similarity filtering is developed and prospectively validated, identifying chlormidazole as a promising repurposing candidate for PCa and demonstrating the value of integrating chemoinformatics with ML for drug repurposing and virtual screening.
Abstract
Despite recent therapeutic advances, treatment options for advanced, therapy-resistant, and metastatic prostate cancer (PCa) remain limited. Here, we developed and prospectively validated an integrated chemoinformatics and machine learning (ML) workflow with ligand-based similarity filtering. Validation on independent external data sets showed that this applicability-domain-guided integration strategy can reduce false positives and improve virtual screening performance. Screening of DrugBank identified five repurposing candidates with confirmed antiproliferative activity in both 2D and 3D PCa models. Among them, the antifungal agent chlormidazole emerged as the most promising candidate, displaying tumor-selective and predominantly cytostatic activity associated with p57 upregulation, reduced Rb phosphorylation, and G1 arrest. Chlormidazole also enhanced the antiproliferative activity of docetaxel in both models, achieving comparable efficacy at substantially lower docetaxel concentrations. These findings identify chlormidazole as a promising repurposing candidate for PCa and demonstrate the value of integrating chemoinformatics with ML for drug repurposing and virtual screening.