Computational methods and tools to predict cytochrome P450 metabolism for drug discovery
Peer reviewed, Journal article
Published version
Åpne
Permanent lenke
https://hdl.handle.net/1956/22821Utgivelsesdato
2019Metadata
Vis full innførselSamlinger
- Department of Chemistry [432]
Originalversjon
https://doi.org/10.1111/cbdd.13445Sammendrag
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.