化合物药物-靶标蛋白互作关联预测算法进展分析

Progress Analysis of Compound-Protein Interactions Prediction Algorithms

药物的使用极大地提高了人类的生存质量。药物的有效性是药物发现研究中的关键环节。药物的有效性通过识别药物与其作用的靶标蛋白来判断。然而,通过高通量筛选的实验方法分析确定化合物药物-靶标蛋白互作关联是一个十分昂贵、耗时且富有挑战性的任务。基于计算方法的化合物药物-靶标蛋白互作关联预测研究具有效率高、成本低的特点,越来越受到人们的重视。相比实验验证方法,化合物药物-靶标蛋白互作关联的计算方法可为药物发现研究后续的生物药学实验提供更为准确的潜在化合物药物-靶标蛋白候选对,达到减少生物实验的时间和成本的目的。本文回顾了近20年来基于计算方法的化合物药物-靶标蛋白互作关联预测算法所涉及的生物医学特征数据、预测方法和技术,并分析研究过程中所面临的生物医学特征数据高维稀疏,以及多源生物医学数据融合程度不高等问题,为进一步研究提供有价值的参考。

Drug application has greatly improved the quality of human life.The effectiveness of drug is a key factor in drug discovery process,and is determined by identifying drug-target interactions.However,it is a very expensive,time-consuming and challenging task to analyse and determine the compound-protein interactions through high-throughput screening experimental methods.The drug discovery research using computational methods are high efficiency and low cost,and it has been paid more and more attention.Compared with the wet-lab experiments,the computational prediction methods of compound-protein interactions can provide more accurate and safe potential candidate drug-target pairs for the subsequent biological experiment,and reduce the spending time and cost of biological experiments in drug discovery process.We review development of compound-protein interactions prediction,as well as the biomedical feature data,prediction algorithms,and technologies in the past two decades.This paper analyzes the problems faced in the research process such as high-dimensional sparsity of biomedical data and insufficient integration of multi-omics biomedical data,and it will provide valuable information for further research.