Computational methods and tools to predict cytochrome P450 metabolism for drug discovery
Jonathan D. Tyzacka b✉, Johannes Kirchmairc d e f g
- aEMBL‐EBI, Wellcome Genome Campus Cambridge UK
- bEMBL-EBI, Wellcome Genome Campus, Cambridge, UK
- cCenter for Bioinformatics Universität Hamburg Hamburg Germany
- dComputational Biology Unit (CBU) University of Bergen Bergen Norway
- eDepartment of Chemistry University of Bergen Bergen Norway
- fComputational Biology Unit (CBU), University of Bergen, Bergen, Norway
- gDepartment of Chemistry, University of Bergen, Bergen, Norway
- ✉Corresponding author
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Abstract
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.
TL;DRThis review presents important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery and summarizes the different approaches taken, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. (Semantic Scholar)