EMBL-EBI, Wellcome Genome Campus, Cambridge, UK.
Department of Chemistry, University of Bergen, Bergen, Norway.
Chem Biol Drug Des. 2019 Apr;93(4):377-386. doi: 10.1111/cbdd.13445. Epub 2019 Jan 15.
In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule-based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.
在这篇综述中，我们介绍了在药物发现背景下，细胞色素 P450（CYP）代谢的计算预测方面的重要、近期进展。我们讨论了 CYP 代谢预测各个方面的计算模型，包括 CYP 底物和抑制剂预测器、代谢部位预测器（即潜在底物中的代谢不稳定部位）和代谢产物结构预测器。我们总结了这些模型所采用的不同方法，例如基于规则的方法、机器学习、数据挖掘、量子化学方法、分子相互作用场和对接。我们强调了每种方法的范围和局限性，并讨论了对药物发现中代谢预测领域的未来影响。
