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dc.contributor.authorTyzack, Jonathan
dc.contributor.authorKirchmair, Johannes
dc.date.accessioned2020-06-23T10:23:52Z
dc.date.available2020-06-23T10:23:52Z
dc.date.issued2019
dc.PublishedTyzack, Kirchmair J. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chemical Biology and Drug Design. 2019;93(4):377-386eng
dc.identifier.issn1747-0285en_US
dc.identifier.issn1747-0277en_US
dc.identifier.urihttps://hdl.handle.net/1956/22821
dc.description.abstractIn 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.en_US
dc.language.isoengeng
dc.publisherWileyen_US
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0eng
dc.titleComputational methods and tools to predict cytochrome P450 metabolism for drug discoveryen_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2020-02-12T16:40:09Z
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2018 The Author(s)en_US
dc.identifier.doihttps://doi.org/10.1111/cbdd.13445
dc.identifier.cristin1654212
dc.source.journalChemical Biology and Drug Design
dc.relation.projectBergens forskningsstiftelse: BFS2017TMT01


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