Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, 53115, Bonn, Germany.
Sci Rep. 2021 Apr 12;11(1):7863. doi: 10.1038/s41598-021-87042-z.
Compounds with defined multi-target activity (promiscuity) play an increasingly important role in drug discovery. However, the molecular basis of multi-target activity is currently only little understood. In particular, it remains unclear whether structural features exist that generally characterize promiscuous compounds and set them apart from compounds with single-target activity. We have devised a test system using machine learning to systematically examine structural features that might characterize compounds with multi-target activity. Using this system, more than 860,000 diagnostic predictions were carried out. The analysis provided compelling evidence for the presence of structural characteristics of promiscuous compounds that were dependent on given target combinations, but not generalizable. Feature weighting and mapping identified characteristic substructures in test compounds. Taken together, these findings are relevant for the design of compounds with desired multi-target activity.
具有明确多靶点活性（混杂性）的化合物在药物发现中起着越来越重要的作用。然而，目前对多靶点活性的分子基础知之甚少。特别是，目前尚不清楚是否存在一般特征化混杂化合物并将其与具有单靶点活性的化合物区分开来的结构特征。我们设计了一个使用机器学习的测试系统，系统地检查可能特征化具有多靶点活性的化合物的结构特征。使用该系统，进行了超过 860,000 次诊断预测。分析提供了令人信服的证据，证明了混杂化合物的结构特征的存在取决于特定的靶标组合，但不具有普遍性。特征加权和映射确定了测试化合物中的特征子结构。总之，这些发现与具有所需多靶点活性的化合物的设计有关。
