论文标题
富集评分：一个更好的定量度量标准，用于评估分子对接模型的富集能力
Enrichment Score: a better quantitative metric for evaluating the enrichment capacity of molecular docking models
论文作者
论文摘要
用于评估称为$ \ textit {logauc} $的富集能力的标准定量度量取决于控制log缩放x轴的最小值的截止参数。除非在给定的ROC曲线中仔细选择此参数，否则会发生以下两个问题之一：（1）仅将ROC曲线的第一个间隔间隔的一小部分抛弃，并且根本不构成指标，或者（2）最初的跨码间间隔对以下组合间间隔的费用造成了极大的贡献。我们通过显示一种简单的方法来根据诱饵的数量来选择截止参数的简单方法来解决此问题，该诱饵数量迫使第一个间隔间隔始终对总价值具有稳定，明智的贡献。 Moreover, we introduce a normalized version of LogAUC known as $\textit{enrichment score}$, which (1) enforces stability by selecting the cutoff parameter in the manner described, (2) yields scores which are more intuitively meaningful, and (3) allows reliably accurate comparison of the enrichment capacities exhibited by different ROC curves, even those produced using different numbers of decoys.最后，我们使用$ \ textit {dude-z} $基准中包含的目标受体tryb1对$ \ textit {dock 3.7} $进行的真实回顾性对接研究的数据证明了富集得分的优势。
The standard quantitative metric for evaluating enrichment capacity known as $\textit{LogAUC}$ depends on a cutoff parameter that controls what the minimum value of the log-scaled x-axis is. Unless this parameter is chosen carefully for a given ROC curve, one of the two following problems occurs: either (1) some fraction of the first inter-decoy intervals of the ROC curve are simply thrown away and do not contribute to the metric at all, or (2) the very first inter-decoy interval contributes too much to the metric at the expense of all following inter-decoy intervals. We fix this problem with LogAUC by showing a simple way to choose the cutoff parameter based on the number of decoys which forces the first inter-decoy interval to always have a stable, sensible contribution to the total value. Moreover, we introduce a normalized version of LogAUC known as $\textit{enrichment score}$, which (1) enforces stability by selecting the cutoff parameter in the manner described, (2) yields scores which are more intuitively meaningful, and (3) allows reliably accurate comparison of the enrichment capacities exhibited by different ROC curves, even those produced using different numbers of decoys. Finally, we demonstrate the advantage of enrichment score over unbalanced metrics using data from a real retrospective docking study performed using the program $\textit{DOCK 3.7}$ on the target receptor TRYB1 included in the $\textit{DUDE-Z}$ benchmark.
