Papers › TransformerCPI: improving compound–protein interaction prediction by sequence-based...
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Lifan Chen, Xiaoqin Tan, Dingyan Wang, Feisheng Zhong, Xiaohong Liu, Tianbiao Yang, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, Mingyue Zheng
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 introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Drug Discovery | BindingDB | TransformerCPI | AUC | 0.937 | #3 of 4 | Archive leaderboard | report |
| Drug Discovery | LIT-PCBA(ALDH1) | TransformerCPI | AUC | 0.694 | #3 of 4 | Archive leaderboard | report |
| Drug Discovery | LIT-PCBA(ESR1_ant) | TransformerCPI | AUC | 0.616 | #2 of 3 | Archive leaderboard | report |
| Drug Discovery | LIT-PCBA(KAT2A) | TransformerCPI | AUC | 0.650 | #3 of 4 | Archive leaderboard | report |
| Drug Discovery | LIT-PCBA(MAPK1) | TransformerCPI | AUC | 0.683 | #3 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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