CypReact: A Software Tool for in Silico Reactant Prediction for Human Cytochrome P450 Enzymes

In silico metabolism prediction requires first predicting whether a specific molecule will interact with one or more specific metabolizing enzymes, then predicting the result of each enzymatic reaction. Here, we provide a computational tool, CypReact, for performing this first task of reactant prediction. Specifically, CypReact takes as input an arbitrary molecule (specified as a SMILES string or a standard SDF file) and any one of the nine of the most important human cytochrome P450 (CYP450) enzymes-CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, or CYP3A4-and accurately predicts whether the query molecule will react with that given CYP450 enzyme. Tests of CypReact, conducted over a data set of 1632 molecules (each considered a plausible reactant) show that it is very effective, with a (cross validation) AUROC (area under the receiver operating characteristic curve) of 0.83-0.92. We also show that CypReact performs significantly better than other reactant prediction tools such as ADMET Predictor and (a reactant-predicting extension of) SMARTCyp, whose average AUROCs are 0.75 and 0.53, respectively. We then applied the learned CypReact models to a previously unseen set of molecules and found that our CypReact did even better and still significantly surpassed the performance of SMARTCyp and ADMET Predictor. These results suggest that CypReact could be an important component of a suite of in silico metabolism prediction tools for accurately predicting the products of Phase I, Phase II, and microbial metabolism in humans. CypReact is available at https://bitbucket.org/Leon_Ti/cypreact.