基于特征的深度学习预测化合物-蛋白质相互作用的研究进展

Research progress of feature-based deep learning for predicting com⁃pound-protein interaction

药物研发过程中,化合物-蛋白质相互作用(compound-protein interaction,CPI)预测是发现苗头化合物、药物重定位等研究的关键技术手段。近年来,深度学习被广泛应用于CPI研究,加速了药物发现中CPI预测的发展。本文重点讨论基于特征的CPI预测模型,首先,介绍了CPI预测中常见的数据库、化合物和蛋白质的典型特征表示方法。根据建模中的关键问题,从多模态和注意力机制两个方面,对基于特征的CPI预测模型展开论述。在此基础上,选取其中12个模型,在3个经典数据集上评估了模型在分类任务和回归任务中的性能。本文总结当前该领域面临的挑战,对未来的发展方向进行展望,为CPI预测方法进一步研究提供思路。

The prediction of compound-protein interaction(CPI)is a critical technological tool for discovering lead compounds and drug repurposing during the process of drug development.In recent years,deep learning has been widely used in CPI research,which has accelerated the development of CPI prediction in drug discovery.This review focuses on feature-based CPI prediction models.First,we described the datasets,as well as typical feature representation methods commonly used for compounds and proteins in CPI prediction.Based on the criti⁃cal problems in modeling,we discussed models for CPI prediction from two perspectives:multimodal features and attention mechanisms.Then,the performance of 12 selected models was evaluated on 3 benchmark datasets for both classification and regression tasks.Finally,the review summarizes the existing challenges in this field and prospects for future directions.We believe that this investigation will provide some reference and insight for fur⁃ther research on CPI prediction.