Drakakis Georgios, Cortés-Ciriano Isidro, Alexander-Dann Ben, Bender Andreas
Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.
Curr Protoc Chem Biol. 2019 Sep;11(3):e73. doi: 10.1002/cpch.73.
The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as early as possible in the drug-discovery process. Here, we provide detailed instructions for capitalizing on bioactivity predictions to elucidate the MoAs of small molecules and infer their underlying phenotypic effects. We illustrate how these predictions can be used to infer the underlying antidepressive effects of marketed drugs. We also provide the necessary functionalities to model cytotoxicity data using single and ensemble machine-learning algorithms. Finally, we give detailed instructions on how to calculate confidence intervals for individual predictions using the conformal prediction framework. © 2019 by John Wiley & Sons, Inc.
药物的作用模式（MoAs）常常未知，因为许多药物是最初从表型筛选中鉴定出的小分子，这就产生了阐明其作用模式的需求。此外，由于不可耐受的毒性，临床前研究中候选药物的高淘汰率推动了计算方法的发展，以便在药物发现过程中尽早预测候选药物的（细胞）毒性。在此，我们提供详细说明，以利用生物活性预测来阐明小分子的作用模式并推断其潜在的表型效应。我们举例说明这些预测如何用于推断上市药物的潜在抗抑郁作用。我们还提供了使用单机器学习算法和集成机器学习算法对细胞毒性数据进行建模的必要功能。最后，我们给出关于如何使用共形预测框架计算单个预测的置信区间的详细说明。© 2019 约翰威立国际出版公司
