Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates
Carmen Esposito, Shuzhe Wang, Udo E. W. Lange, Frank Oellien, Sereina Riniker
Abstract
models have been developed based on structural and physicochemical descriptors. In this study, we investigate the use of molecular dynamics fingerprints (MDFPs) as an orthogonal descriptor for the training of machine learning (ML) models to classify small molecules into substrates and nonsubstrates of P-gp. MDFPs encode the information from short MD simulations of the molecules in different environments (water, membrane, or protein pocket). The performance of the MDFPs, evaluated on both an in-house dataset (3930 compounds) and a public dataset from ChEMBL (1114 compounds), is compared to that of commonly used 2D molecular descriptors, including structure-based and property-based descriptors. We find that all tested classifiers interpolate well, achieving high accuracy on chemically diverse subsets. However, by challenging the models with external validation and prospective analysis, we show that only tree-based ML models trained on MDFPs or property-based descriptors generalize well to regions of the chemical space not covered by the training set.