MOLECULE: Molecular-dynamics and Optimized deep Learning for Entropy-regularized Classification and Uncertainty-aware Ligand Evaluation

Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models’ predictions. Indeed, we previously demonstrated that combining molecular dynamics (MD)-derived descriptors with ML models allows to effectively classify kinase ligands as allosteric or orthosteric. Extending this approach, we curated a wide and diverse kinase data set (comprising 280 experimentally resolved structures) to train and evaluate a new dual-modal deep neural network classifier, which is tailored to process separately and efficiently the dynamical and structural data to predict the mode of action of a compound. The developed model demonstrated robust classification performance, effective uncertainty handling, and underscored the critical importance of incorporating protein dynamics data. Remarkably, our method maintained high performance even with imputed dynamics data, enabling rapid compound screening and prioritization, without the need for extensive MD simulations.