计算机科学
人工智能
机器学习
任务(项目管理)
药物发现
人工神经网络
数据挖掘
序列(生物学)
生物信息学
生物
遗传学
经济
管理
作者
Lifan Chen,Xin Tan,Dingyan Wang,Feisheng Zhong,Xiaohong Liu,Tianjun Yang,Xiaomin Luo,Kaixian Chen,Hualiang Jiang,Mingyue Zheng
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2020-05-19
卷期号:36 (16): 4406-4414
被引量:187
标识
DOI:10.1093/bioinformatics/btaa524
摘要
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 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. Availability and implementation https://github.com/lifanchen-simm/transformerCPI.
科研通智能强力驱动
Strongly Powered by AbleSci AI