Improved Drug-target Interaction Prediction with Intermolecular Graph
Transformer
- URL: http://arxiv.org/abs/2110.07347v2
- Date: Fri, 15 Oct 2021 15:21:48 GMT
- Title: Improved Drug-target Interaction Prediction with Intermolecular Graph
Transformer
- Authors: Siyuan Liu, Yusong Wang, Tong Wang, Yifan Deng, Liang He, Bin Shao,
Jian Yin, Nanning Zheng, Tie-Yan Liu
- Abstract summary: We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture.
Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively.
IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
- Score: 98.8319016075089
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The identification of active binding drugs for target proteins (termed as
drug-target interaction prediction) is the key challenge in virtual screening,
which plays an essential role in drug discovery. Although recent deep
learning-based approaches achieved better performance than molecular docking,
existing models often neglect certain aspects of the intermolecular
information, hindering the performance of prediction. We recognize this problem
and propose a novel approach named Intermolecular Graph Transformer (IGT) that
employs a dedicated attention mechanism to model intermolecular information
with a three-way Transformer-based architecture. IGT outperforms
state-of-the-art approaches by 9.1% and 20.5% over the second best for binding
activity and binding pose prediction respectively, and shows superior
generalization ability to unseen receptor proteins. Furthermore, IGT exhibits
promising drug screening ability against SARS-CoV-2 by identifying 83.1% active
drugs that have been validated by wet-lab experiments with near-native
predicted binding poses.
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