Nonadditivity Analysis

We introduce the statistics behind a novel type of SAR analysis named “nonadditivity analysis”. On the basis of all pairs of matched pairs within a given data set, the approach analyzes whether the same transformations between related molecules have the same effect, i.e., whether they are additive. Assuming that the experimental uncertainty is normally distributed, the additivities can be analyzed with statistical rigor and sets of compounds can be found that show significant nonadditivity. Nonadditivity analysis can not only detect nonadditivity, potential SAR outliers, and sets of key compounds but also allow estimating an upper limit of the experimental uncertainty in the data set. We demonstrate how complex SAR features that inform medicinal chemistry can be found in large SAR data sets. Finally, we show how the upper limit of experimental uncertainty for a given biochemical assay can be estimated without the need for repeated measurements of the same protein–ligand system.本文介绍了一种名为非加和性分析（nonadditivity analysis）的新型结构-活性关系（Structure-Activity Relationship，SAR）分析方法背后的统计学原理。该方法基于给定数据集中的所有匹配分子对，分析相关分子间的相同结构修饰是否具有一致的效应，即是否符合加和性准则。假设实验不确定度服从正态分布，即可通过严谨的统计学手段开展加和性分析，并筛选出呈现显著非加和性的化合物集合。非加和性分析不仅可检测非加和性现象、潜在的SAR异常值以及关键化合物集合，还可估算数据集中实验不确定度的上限。本文演示了如何在大规模SAR数据集中挖掘可为药物化学研究提供参考的复杂SAR特征。最后，本文展示了无需对同一蛋白质-配体体系进行重复测量，即可估算给定生化测定中实验不确定度上限的方法。