Flexible drug-target interaction prediction with interactive information extraction and trade-off

计算机科学 萃取(化学) 信息抽取 机器学习 人工智能 药品 药物与药物的相互作用 数据挖掘 药理学 色谱法 化学 医学
作者
Yunfei He,Chenyuan Sun,Li Meng,Yiwen Zhang,Rui Mao,Fei Yang
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:249: 123821-123821 被引量:4
标识
DOI:10.1016/j.eswa.2024.123821
摘要

Drug-target interaction (DTI) prediction refers to the use of computational methods and models to predict the interaction between drugs and biological targets. DTI can help researchers understand the mechanism of action of drugs, discover new drug targets, and screen drug candidates. Recently, a large number of DTI models integrating deep drug-target interaction features have emerged to make up for the dilemma of incomplete information on shallow drug and target features. However, these models ignore the challenge of overlapping interaction information by simply integrating deep interaction information. This paper proposes a flexible DTI with interactive information extraction and trade-off (FDTIIT) to address the above challenges. The main idea of FDTIIT is to use flexible mutual attention to extract interaction information about drugs and targets, and then limit the dependence between them to avoid redundant information. Specifically, FDTIIT mainly includes three parts: drug and target representation, drug-target interactive information extraction, and drug-target interactive information trade-off. Among them, the drug and target representation module mainly uses the graph convolutional network and convolutional neural network to learn the representation of drugs and targets. Then, the drug-target interactive information extraction module extracts the drug information hidden in the target and the target information hidden in the drug based on mutual attention. To avoid possible information overlap between drug representation and target representation after the fusion of interaction information, FDTIIT designs an interactive information trade-off module. This module limits the dependence between drug and target representation, providing more comprehensive information to support high-performance drug-target interaction prediction. Multiple experiments designed on three publicly available datasets validated FDTIIT's effectiveness.
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