论文标题
回归富集表面：虚拟药物筛查模型的简单分析技术
Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models
论文作者
论文摘要
我们提出了一种新方法，以了解模型在虚拟药物筛查任务中的性能。尽管大多数虚拟筛选问题是排名和分类之间的混合，但通常将模型作为回归模型进行训练，该模型提出了一个需要选择截止或排名度量的问题。我们的方法，回归富集表面（RES）是基于虚拟筛查的目标：检测尽可能多的表现最佳治疗方法。我们概述了虚拟筛选绩效指标和RES背后的想法的历史。我们提供了一个Python软件包，以及有关如何实施和解释结果的详细信息。
We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and classification, the models are typically trained as regression models presenting a problem requiring either a choice of a cutoff or ranking measure. Our method, regression enrichment surfaces (RES), is based on the goal of virtual screening: to detect as many of the top-performing treatments as possible. We outline history of virtual screening performance measures and the idea behind RES. We offer a python package and details on how to implement and interpret the results.
