药物-靶点相互作用预测的计算方法综述

Survey on Computational Approaches for Drug-Target Interaction Prediction

药物-靶点相互作用预测旨在发现可作用于特定蛋白质的潜在药物,在药物重定位、药物副作用预测、多重药理学和耐药性的研究中都发挥着重要作用。随着计算机处理能力的进步和计算算法的不断更新,药物-靶点相互作用预测的计算方法展现出时间短、成本低、精度高、范围广的优势,受到了广泛的关注,并取得了显著的进展。为了梳理其研究发展历程,探讨未来的研究方向,就药物-靶点相互作用预测的背景和意义进行简要概述;将方法分为基于分子对接、基于药物结构、基于文本挖掘和基于化学基因组四类进行综述,并对每类方法进行对比分析,详细阐述每类方法的数据需求及应用场景;对现有研究存在的局限性和面临的挑战进行讨论,展望未来的研究方向,为后续研究提供参考和借鉴。

Drug-target interaction prediction aims to discover potential drugs acting on specific proteins,and plays an important role in drug repositioning,drug side effect prediction,polypharmacology and drug resistance research.With the advancement of computer processing and the continuous updating of computing algorithms,the computational drug-target interaction prediction has shown the advantages of short time,low cost,high precision and wide range,which has received extensive attention and made remarkable progress.In order to sort out the development history and explore the future research direction,the background and significance of drug-target interaction prediction are firstly introduced in brief.Secondly,the methods are classified into four types:molecular docking-based,drug structure-based,text mining-based and chemogenomic-based methods.A comparative analysis of each method is carried out,and the data requirements and application scenarios for each type of methods are described in detail.Finally,the limitations and challenges of the existing research are discussed,and the future research directions are prospected to provide references for follow-up research.