WhichP450: a multi-class categorical model
P. A. Hunt, M. D. Segall & J. D. Tyzack, J. Comput.-Aided Mol. Des. 2018, 32, pp537–546
This paper describes the underlying methods and validation of a model predicting the most likely Cytochrome P450 isoforms responsible for metabolism of a compound. The model makes up part of StarDrop’s P450 module.
In the development of novel pharmaceuticals, the knowledge of how many, and which, Cytochrome P450 isoforms are involved in the phase I metabolism of a compound is important. Potential problems can arise if a compound is metabolised predominantly by a single isoform in terms of drug-drug interactions or genetic polymorphisms that would lead to variations in exposure in the general population. Combined with models of regioselectivities of metabolism by each isoform, such a model would also aid in the prediction of the metabolites likely to be formed by P450-mediated metabolism.
We describe the generation of a multi-class random forest model. It predicts which, out of a list of the 7 leading Cytochrome P450 isoforms, would be the major metabolising isoforms for a novel compound. The model has a 76% success rate with a top-1 criterion and an 88% success rate for a top-2 criterion. It shows significant enrichment over randomised models.
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