In Bioinformatics (Oxford, England)
MOTIVATION : Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional (3D) 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.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
AVAILABILITY AND IMPLEMENTATION : https://github.com/lifanchen-simm/transformerCPI.
Chen Lifan, Tan Xiaoqin, Wang Dingyan, Zhong Feisheng, Liu Xiaohong, Yang Tianbiao, Luo Xiaomin, Chen Kaixian, Jiang Hualiang, Zheng Mingyue