期刊
CHEMICAL BIOLOGY & DRUG DESIGN
卷 93, 期 4, 页码 377-386出版社
WILEY
DOI: 10.1111/cbdd.13445
关键词
cytochrome P450; drug discovery; enzyme-ligand interaction; machine learning; metabolism; metabolite structures; prediction; reactivity; sites of metabolism
资金
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [KI 2085/1-1]
- Bergen Research Foundation (BFS) [BFS2017TMT01]
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.
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