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Protein-ligand binding affinity prediction with edge awareness and supervised attention.


ABSTRACT: Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization.

SUBMITTER: Gu Y 

PROVIDER: S-EPMC9860494 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Protein-ligand binding affinity prediction with edge awareness and supervised attention.

Gu Yuliang Y   Zhang Xiangzhou X   Xu Anqi A   Chen Weiqi W   Liu Kang K   Wu Lijuan L   Mo Shenglong S   Hu Yong Y   Liu Mei M   Luo Qichao Q  

iScience 20221228 1


Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, wher  ...[more]

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