RT Journal Article
SR Electronic
T1 Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalize To Unseen Target Classes, And Highlight Important Binding Interactions
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.03.06.979625
DO 10.1101/2020.03.06.979625
A1 Scantlebury, Jack
A1 Brown, Nathan
A1 Von Delft, Frank
A1 Deane, Charlotte M.
YR 2020
UL http://biorxiv.org/content/early/2020/03/15/2020.03.06.979625.abstract
AB Current deep learning methods for structure-based virtual screening take the structures of both the protein and the ligand as input but make little or no use of the protein structure when predicting ligand binding. Here we show how a relatively simple method of dataset augmentation forces such deep learning methods to take into account information from the protein. Models trained in this way are more generalisable (make better predictions on protein-ligand complexes from a different distribution to the training data). They also assign more meaningful importance to the protein and ligand atoms involved in binding. Overall, our results show that dataset augmentation can help deep learning based virtual screening to learn physical interactions rather than dataset biases.