The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability

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• 作者：Julio Cesar Dias Lopes ; Fábio Mendes dos Santos…
• 关键词：Power metric (PM) ; Virtual screening ; Metric ; Model performance ; Enrichment factor ; Area under the curve (AUC) ; Receiver operating curve enrichment factor (ROCE) ; Correct classification rate (CCR) ; Matthews correlation coefficient (MCC) ; Cohen’s kappa coefficient (CKC) ; Relative enrichment factor (REF)
• 刊名：Journal of Cheminformatics
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：9
• 期：1
• 全文大小：2290KB
• 刊物类别：Physics and Astronomy
• 刊物主题：Computer Applications in Chemistry; Documentation and Information in Chemistry; Theoretical and Computational Chemistry; Computational Biology/Bioinformatics;
• 出版者：Springer International Publishing
• ISSN：1758-2946
• 卷排序：9

文摘

A new metric for the evaluation of model performance in the field of virtual screening and quantitative structure–activity relationship applications is described. This metric has been termed the power metric and is defined as the fraction of the true positive rate divided by the sum of the true positive and false positive rates, for a given cutoff threshold. The performance of this metric is compared with alternative metrics such as the enrichment factor, the relative enrichment factor, the receiver operating curve enrichment factor, the correct classification rate, Matthews correlation coefficient and Cohen’s kappa coefficient. The performance of this new metric is found to be quite robust with respect to variations in the applied cutoff threshold and ratio of the number of active compounds to the total number of compounds, and at the same time being sensitive to variations in model quality. It possesses the correct characteristics for its application in early-recognition virtual screening problems.