Improved drug-target interaction prediction with intermolecular graph transformer.
Brief Bioinform
; 23(5)2022 09 20.
Article
in En
| MEDLINE
| ID: mdl-35514186
ABSTRACT
The identification of active binding drugs for target proteins (referred to 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 achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the 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 (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https//github.com/microsoft/IGT-Intermolecular-Graph-Transformer.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
COVID-19
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Brief Bioinform
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2022
Type:
Article
Affiliation country:
China