Issue 13, 2022

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, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.

Graphical abstract: PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions

Supplementary files

Article information

Article type
Edge Article
Submitted
13 dic. 2021
Accepted
06 feb. 2022
First published
07 feb. 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2022,13, 3661-3673

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

S. Moon, W. Zhung, S. Yang, J. Lim and W. Y. Kim, Chem. Sci., 2022, 13, 3661 DOI: 10.1039/D1SC06946B

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