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

ABSTRACT: 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 demonst

SUBMITTER: Cucchi I 

PROVIDER: S-EPMC12461829 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature