Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure–promiscuity relationships.

中文翻译：

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系统数据分析和诊断机器学习揭示具有单目标和多目标活性的化合物之间的差异

具有多靶点活性的小分子能够触发多药理作用，并且在药物发现中具有很高的兴趣。与单一目标化合物相比，滥交还会影响药物分布和药效学并改变 ADMET 特征。目前对区分具有单靶点和多靶点活性的化合物的特征知之甚少。在系统数据分析的基础上，我们组装了大量具有针对相关或功能不同目标的活性的混杂化合物以及具有单一目标活性的相应化合物。机器学习以惊人的高准确度预测了混杂的化合物。分子相似性分析结合不同条件下的控制计算表明，准确预测很大程度上取决于不同类别化合物之间的结构最近邻关系。我们还发现，大部分对相关或不相关目标具有活性的混杂化合物和相应的单目标化合物形成具有不同化学空间覆盖范围的类似物系列，这进一步使预测合理化。此外，对功能不同类别的蛋白质具有活性的化合物通常对其他混杂化合物未涵盖的独特靶标具有活性。