StackCPA: A stacking model for compound-protein binding affinity prediction based on pocket multi-scale features

Accurately predicting compound-protein binding affinity is a crucial task in drug discovery. Computational models offer the advantages of short time, low cost and safety compared to traditional drug development. Pocket is the key binding region of the protein, which provides invaluable information for drug repositioning and drug design. In this study, we propose an ensemble learning model, called StackCPA, to predict the compound-protein binding affinity. The model integrates multi-scale features of protein pocket and compound through a transfer learning strategy. The protein pocket is described in a fine-grained way by atomic level, residue level and subdomain level. The proposed model StackCPA is evaluated on three binding affinity benchmark datasets. The experiment results show that StackCPA achieves the best performance on all the three datasets in comparison with other state-of-the-art deep learning models. The ablation study shows that the protein pocket can provide sufficient information for affinity prediction and its multi-scale features enable the model to further improve the prediction performance. In addition, the case study for epidermal growth factor receptor erbB1 (EGFR) indicates that StackCPA could serve as an effective tool for drug repurposing. Source codes and data of StackCPA are available at https://github.com/CSUBioGroup/StackCPA.

Automated summary

A study proposes an ensemble learning model called StackCPA, which accurately predicts compound-protein binding affinity by integrating multi-scale features of protein pocket and compound through a transfer learning strategy. The experiment results show that StackCPA outperforms other state-of-the-art deep learning models on three benchmark datasets. The protein pocket provides sufficient information for affinity prediction, and its multi-scale features further improve the prediction performance.