@article{liu2022improved,
author = {Liu, Siyuan and Wang, Yusong and Deng, Yifan and He, Liang and Shao, Bin and Yin, Jian and Zheng, Nanning and Liu, Tie-Yan and Wang, Tong},
title = {Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer},
year = {2022},
month = {May},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/improved-drug-target-interaction-prediction-with-intermolecular-graph-transformer/},
journal = {Briefings in Bioinformatics},
}