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A strategy on the definition of applicability domain of model based on population analysis

文献类型： 外文期刊

作者： Yun, Yong-Huan 1 ; Wu, Dong-Ming 1 ; Li, Guang-Yi 1 ; Zhang, Qiao-Yan 1 ; Yang, Xia 1 ; Li, Qin-Fen 1 ; Cao, Dong-Shen 1 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Inst Environm & Plant Protect, Haikou 571101, Hainan, Peoples R China

2.Minist Agr, Expt Stn Agr Environm Sci Observat Danzhou, Danzhou, Hainan, Peoples R China

3.Cent S Univ, Xiangya Sch Pharmaceut Sci, Changsha 410013, Hunan, Peoples R C

关键词： Applicability domain;QSAR;Population analysis;Model population analysis;Approach population analysis

期刊名称：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS （ 影响因子：3.491； 五年影响因子：3.839 ）

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收录情况： SCI

摘要： AbstractIn recent years, there have been growing concerns about quality evaluation of predictions of developed quantitative structure-activity relationship (QSAR) models. Well-defined applicability domain (AD) is very crucial in the validation of QSAR models as stated in the third principle of Organization for Economic Co-operation and Development (OECD). In this study, a new perspective on defining AD of model based on population analysis (PA) strategy, including model population analysis (MPA) and approach population analysis (APA), was proposed. MPA employed classical AD approaches to define AD with a vast amount of sub-datasets derived from training set. On the basis of MPA, the classical AD approaches could distinguish part of the samples that cannot be distinguished by full training samples. APA was then used to get a union of all results generated by the used AD approaches to give a consensus list of samples as falling outside the AD. In order to investigate the performance of PA strategy in defining AD with the classical AD approaches, two QSAR datasets were used. The results show that implementing PA strategy can assist three classical AD approaches to distinguish the additional samples that cannot be distinguished using full training dataset. When excluding the additional samples, the root mean square error of prediction of test set decreased, suggesting that PA strategy has a potential to distinguish the samples that cannot be reliably predicted.Highlights?A new perspective on defining AD of model basedon PA strategy, including MPA and APA, was proposed.?MPA assisted classical AD approaches to distinguish the additional samples that cannot be done using full training dataset.?APA got a union of all results generated by AD approaches to give a consensus list of samples falling outside the AD.]]>

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