FOTF-CPI: A compound-protein interaction prediction transformer based on the fusion of optimal transport fragments

Zeyu Yin; Yu Chen; Yajie Hao; Sanjeevi Pandiyan; Jinsong Shao; Li Wang

doi:10.1016/j.isci.2023.108756

FOTF-CPI: A compound-protein interaction prediction transformer based on the fusion of optimal transport fragments

iScience. 2023 Dec 15;27(1):108756. doi: 10.1016/j.isci.2023.108756. eCollection 2024 Jan 19.

Authors

Zeyu Yin¹, Yu Chen¹, Yajie Hao¹, Sanjeevi Pandiyan², Jinsong Shao¹, Li Wang^{1

2}

Affiliations

¹ School of Information Science and Technology, Nantong University, Nantong 226001, China.
² Research Center for Intelligent Information Technology, Nantong University, Nantong 226001, China.

Abstract

Compound-protein interaction (CPI) affinity prediction plays an important role in reducing the cost and time of drug discovery. However, the interpretability of how fragments function in CPI is impacted by the fact that current methods ignore the affinity relationships between fragments of compounds and fragments of proteins in CPI modeling. This article introduces an improved Transformer called FOTF-CPI (a Fusion of Optimal Transport Fragments compound-protein interaction prediction model). We use an optimal transport-based fragmentation approach to improve the model's understanding of compound and protein sequences. Additionally, a fused attention mechanism is employed, which combines the features of fragments to capture full affinity information. This fused attention redistributes higher attention scores to fragments with higher affinity. Experimental results show FOTF-CPI achieves an average 2% higher performance than other models on all three datasets. Furthermore, the visualization confirms the potential of FOTF-CPI for drug discovery applications.

Keywords: Biocomputational method; Computational bioinformatics; In silico biology.