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Recent Advances in QSAR and Their Applications in Predicting the Activities of Chemical Molecules,Peptides and Proteins for Drug Design

机译:QSAR的最新进展及其在预测用于药物设计的化学分子,肽和蛋白质活性中的应用

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This review is to summarize three new QSAR (quantitative structure-activity relationship) methods recently developed in our group and their applications for drug design.Based on more solid theoretical models and advanced mathematical techniques,the conventional QSAR technique has been recast in the following three aspects.(1) In the fragment-based two dimensional QSAR,or abbreviated as FB-QSAR,the molecular structures in a family of drug candidates are divided into several fragments according to the substitutes being investigated.The bioactivities of drug candidates are correlated with physicochemical properties of the molecular fragments through two sets of coefficients:one is for the physicochemical properties and the other for the molecular fragments.(2) In the multiple field three dimensional QSAR,or MF-3D-QSAR,more molecular potential fields are integrated into the comparative molecular field analysis (CoMFA) through two sets of coefficients:one is for the potential fields and the other for the Cartesian three dimensional grid points.(3) In the AABPP (amino acid-based peptide prediction),the bioactivities of peptides or proteins are correlated with the physicochemical properties of all or partial residues of the sequence through two sets of coefficients:one is for the physicochemical properties of amino acids and the other for the weight factors of the residues.Meanwhile,an iterative double least square (IDLS) technique is developed for solving the two sets of coefficients in a training dataset alternately and iteratively.Using the two sets of coefficients,one can predict the bioactivity of a query peptide,protein,or drug candidate.Compared with the old methods,the new QSAR approaches as summarized in this review possess machine learning ability,can remarkably enhance the prediction power,and provide more structural information.Meanwhile,the future challenge and possible development in this area have been briefly addressed as well.
机译:这篇综述总结了我们小组最近开发的三种新的QSAR(定量结构-活性关系)方法及其在药物设计中的应用。在更扎实的理论模型和先进的数学技术的基础上,以下三种方法已改写为传统的QSAR技术(1)在基于片段的二维QSAR中,简称FB-QSAR,根据所研究的替代物将候选药物家族的分子结构分为几个片段。候选药物的生物活性与分子碎片的理化性质通过两套系数来表示:一组系数代表理化性质,另一组系数代表分子碎片。(2)在多场三维QSAR或MF-3D-QSAR中,更多的分子势场被整合通过两组系数进入比较分子场分析(CoMFA):一组用于势场,另一组用于势场(3)在基于氨基酸的肽预测AABPP中,肽或蛋白质的生物活性与序列的全部或部分残基的理化特性通过两组系数相关:一种是氨基酸的理化特性,另一种是氨基酸残基的权重因子。同时,开发了一种迭代双最小二乘(IDLS)技术来交替和迭代地求解训练数据集中的两组系数。两组系数可以预测一个查询肽,蛋白质或候选药物的生物活性。与旧方法相比,本文综述的新QSAR方法具有机器学习能力,可以显着增强预测能力,并提供同时,还简要介绍了该领域的未来挑战和可能的发展。

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