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

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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.
Publisher
ROYAL SOC CHEMISTRY
Issue Date
2022-03
Language
English
Article Type
Article
Citation

CHEMICAL SCIENCE, v.13, no.13, pp.3661 - 3673

ISSN
2041-6520
DOI
10.1039/d1sc06946b
URI
http://hdl.handle.net/10203/292614
Appears in Collection
CH-Journal Papers(저널논문)
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