PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions.

ABSTRACT: Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstra

SUBMITTER: Moon S 

PROVIDER: S-EPMC8966633 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature