Interaction fingerprints (IFP) have been repeatedly shown to be valuable tools in virtual screening to identify novel hit compounds that can subsequently be optimized to drug candidates. As a complementary method to ligand docking, IFPs can be applied to quantify the similarity of predicted binding poses to a reference binding pose. For this purpose, a large number of similarity metrics can be applied, and various parameters of the IFPs themselves can be customized. In a large-scale comparison, we have assessed the effect of similarity metrics and IFP configurations to a number of virtual screening scenarios with ten different protein targets and thousands of molecules. Particularly, the effect of considering general interaction definitions (such as Any Contact, Backbone Interaction and Sidechain Interaction), the effect of filtering methods and the different groups of similarity metrics were studied. The performances were primarily compared based on AUC values, but we have also used the original similarity data for the comparison of similarity metrics with several statistical tests and the novel, robust sum of ranking differences (SRD) algorithm. With SRD, we can evaluate the consistency (or concordance) of the various similarity metrics to an ideal reference metric, which is provided by data fusion from the existing metrics. Different aspects of IFP configurations and similarity metrics were examined based on SRD values with analysis of variance (ANOVA) tests. A general approach is provided that can be applied for the reliable interpretation and usage of similarity measures with interaction fingerprints. Metrics that are viable alternatives to the commonly used Tanimoto coefficient were identified based on a comparison with an ideal reference metric (consensus). A careful selection of the applied bits (interaction definitions) and IFP filtering rules can improve the results of virtual screening (in terms of their agreement with the consensus metric). The open-source Python package FPKit was introduced for the similarity calculations and IFP filtering; it is available at: https://github.com/davidbajusz/fpkit .

中文翻译：

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生命超出谷本系数：相互作用指纹的相似性度量。

交互指纹（IFP）已被反复证明是虚拟筛选中识别新型命中化合物的有价值的工具，这些化合物随后可针对药物候选者进行优化。作为配体对接的补充方法，IFP可以用于量化预测的结合姿势与参考结合姿势的相似性。为此，可以应用大量相似性度量，并且可以定制IFP本身的各种参数。在大规模比较中，我们评估了相似性指标和IFP配置对许多具有十种不同蛋白质靶标和数千种分子的虚拟筛选方案的影响。特别是考虑一般交互定义（例如任何接触，骨干交互和侧链交互）的效果，研究了过滤方法的效果以及不同组的相似性指标。主要根据AUC值比较性能，但我们也使用原始的相似性数据进行相似性指标的比较，并进行了一些统计测试和新颖，可靠的排名差异总和（SRD）算法。借助SRD，我们可以评估各种相似性指标与理想参考指标的一致性（或一致性），理想参考指标是通过现有指标的数据融合来提供的。基于SRD值和方差分析（ANOVA）测试，检查了IFP配置和相似性指标的不同方面。提供了一种通用方法，该方法可以用于具有交互指纹的相似性度量的可靠解释和使用。基于与理想参考指标（共识）的比较，确定了可以替代常用Tanimoto系数的指标。仔细选择所应用的位（交互定义）和IFP过滤规则可以改善虚拟筛选的结果（就它们与共识度量的一致性而言）。引入了开源Python软件包FPKit，用于相似度计算和IFP过滤。可从以下网址获得：https://github.com/davidbajusz/fpkit。仔细选择所应用的位（交互定义）和IFP过滤规则可以改善虚拟筛选的结果（就它们与共识度量的一致性而言）。引入了开源Python软件包FPKit，用于相似度计算和IFP过滤。可从以下网址获得：https://github.com/davidbajusz/fpkit。仔细选择所应用的位（交互定义）和IFP过滤规则可以改善虚拟筛选的结果（就它们与共识度量的一致性而言）。引入了开源Python软件包FPKit，用于相似度计算和IFP过滤。可从以下网址获得：https://github.com/davidbajusz/fpkit。