TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Shanghai Institute of Materia Medica · University of Chinese Academy of Sciences · +2 more institutions
Abstract
MOTIVATION: Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance. RESULTS: To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and…
Citation impact
- FWCI
- 28.28
- Percentile
- 100%
- References
- 41
Authors
10- LCLifan Chen
Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences, State Key Laboratory of Drug Research
- XTXiaoqin Tan
Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences, State Key Laboratory of Drug Research
- DWDingyan Wang
Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences, State Key Laboratory of Drug Research
- FZFeisheng Zhong
Shanghai Institute of Materia Medica, University of Chinese Academy of Sciences, State Key Laboratory of Drug Research
- XLXiaohong Liu
ShanghaiTech University, Shanghai Institute of Materia Medica, State Key Laboratory of Drug Research
Topics & keywords
- Mechanism (biology)
- Computer science
- Sequence (biology)
- Artificial intelligence
- Sequence learning
- Deep learning
- Computational biology
- Machine learning