数量结构-活动关系
珊瑚
软件
计算机科学
计算生物学
生物
生态学
机器学习
程序设计语言
作者
Alla P. Toropova,Ivan Raška,Alla P. Toporova,Mária Rašková
出处
期刊:Current Drug Metabolism
[Bentham Science]
日期:2017-03-06
卷期号:18 (6)
被引量:11
标识
DOI:10.2174/1389200218666170301105916
摘要
The CORAL software has been developed as a tool to build up quantitative structure- activity relationships (QSAR) for various endpoints.The task of the present work was to estimate and to compare QSAR models for biochemical activity of various therapeutic agents, which are built up by the CORAL software.The Monte Carlo technique gives possibility to build up predictive model of an endpoint by means of selection of so-called correlation weights of various molecular features extracted from simplified molecular input-line entry system (SMILES). Descriptors calculated with these weights are basis for building up correlations "structure - endpoint".Optimal descriptors, which are aimed to predict values of endpoints with apparent influence upon metabolism are crytically compared in aspect of their robustness and heuristic potential. Arguments which are confirming the necessity of reformulation of basics of QSARs are listed: (i) each QSAR model is stochastic experiment. The result of this experiment is defined by distribution into the training set and validation set; (ii) predictive potential of a model should be checked up with a group of different splits; and (iii) only model stochastically stable for a group of splits can be estimated as a reliable tool for the prediction. Examples of the improvement of the models previously suggested are demonstrated.The current version of the CORAL software remains a convenient tool to build up predictive models. The Monte Carlo technique involved for the software confirms the principle "QSAR is a random event" is important paradigm for the QSPR/QSAR analyses.
科研通智能强力驱动
Strongly Powered by AbleSci AI