1. Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account
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Ben Alexander-Dann, Georgios Drakakis, Andreas Bender, and Isidro Cortes-Ciriano
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0301 basic medicine ,Databases, Factual ,business.industry ,Computer science ,Drug candidate ,General Medicine ,010402 general chemistry ,Machine learning ,computer.software_genre ,chEMBL ,01 natural sciences ,0104 chemical sciences ,Machine Learning ,Small Molecule Libraries ,Inhibitory Concentration 50 ,03 medical and health sciences ,030104 developmental biology ,Pharmaceutical Preparations ,Mechanism of action ,medicine ,Artificial intelligence ,Polypharmacology ,medicine.symptom ,business ,computer - Abstract
© 2019 John Wiley & Sons, Inc. 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.
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