Application of machine learning and molecular docking to the modelling and design of HDAC6/ROCK dual inhibitors with in vitro experimental validation

Abstract

Background & Aims: Biliary tract cancer (BTC) is a rare but aggressive malignancy with limited treatment options and poor prognosis, necessitating novel therapeutic strategies. Histone deacetylases (HDACs) play a critical role in the epigenetic regulation of BTC progression, and HDAC inhibitors have shown promise in preclinical studies. However, their clinical efficacy as monotherapies remains limited, driving the exploration of multitarget approaches. Recent studies by Djokovic et al. highlight the potential of combining HDAC and Rho-associated protein kinase (ROCK) inhibitors as an antimetastatic strategy. This study aimed to develop machine learning (ML) models for predicting the activity of HDAC6 and ROCK2 inhibitors and to apply these models in the design and evaluation of novel dual HDAC/ROCK inhibitors. Materials & Methods: Datasets for HDAC6 and ROCK2 inhibitors were extracted from the ChEMBL database, preprocessed, and used to calculate 2D molecular descriptors. Predictive models were developed using random forest regression to establish quantitative structure–activity relationship (QSAR) models for both targets. The models were applied for virtual screening of newly designed dual inhibitors, followed by molecular docking studies to assess binding potential. Results: The ML-driven QSAR models demonstrated high predictive accuracy for HDAC6 and ROCK2 inhibitors. Virtual screening identified promising dual HDAC/ROCK candidates, which were further evaluated through molecular docking. The top candidates were synthesized and tested in enzyme assays confirming a lead compound with potent activity, accurately predicted by the ML models. Further in vitro studies in BTC cell lines are ongoing to assess anticancer efficacy. Conclusion: This study presents an ML-guided approach for the rational design of dual HDAC6/ROCK inhibitors, offering a promising therapeutic strategy for BTC. The integration of computational modeling with experimental validation demonstrates the potential of AI-driven drug discovery in oncology.Monothematic Conference, Liquid Biopsy in BTC: from bench to bedside, May 22-23, 2025, Mallorca, Spai