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Virtual screening is a rapidly developing field with substantial impact on drug discovery and has emerged as one of the most powerful tools for eliminating the need for time-consuming experimental assays. The performance of virtual screening methods is generally evaluated by how well they can identify the lead compound or by the accuracy in selecting compounds from the top ranked subset. A central issue in virtually screening is that the performance of a single method is currently evaluated by one of these criteria in isolation of the other. The regression analysis of the compound screening results for ranking the compound sets by each of these criteria is performed as standard practice today. The nonparametric (rank-based) evaluation is inherently limited because of its non-independence between the rankings. Usually the ranking depends strongly on the choice of the standard for measuring the order of the ranks among the groups. In addition, when comparing the performance of two different virtual screening methods, the rank criterion has to be transformed into a quantitative one. In this study, we have presented a method that allows simultaneous analysis of multiple performance criteria in order to provide a deeper insight into the relative performance of different virtual screening methods. This paper applies a unified framework to the evaluation of virtual screening methods, and makes it possible to rank methods based on the dependence of their performance on the properties of the host molecule.Q:
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