Machine Learning Models and a Web Portal for Predicting Cytochrome P450 Activity

Cytochrome P450 (CYP) family of enzymes plays an integral role in drug metabolism and excretion. This application note describes the development of a novel computational CYP profiler (CYP-Pro) as a drug development tool. To enable new model development, we integrated and curated the largest, to the best of our knowledge, dataset comprising 26587 entries, including both inhibitors and substrates of CYP2D6, CYP3A4, and CYP2C9. We have built and externally validated Quantitative Structure-Activity Relationship (QSAR) models that can accurately predict whether molecules of interest are expected to be inhibitors or substrates. The models were assessed mainly by Positive Predictive Value (PPV), which ranged between 0.14 and 0.92. CYP-Pro showed the highest accuracy in predicting compounds selectively metabolized by CYP3A4 alone or by CYP2D6 and CYP2C9 without CYP3A4 involvement. All models are incorporated into the previously developed PhaKinPro portal (https://phakinpro.mml.unc.edu). CYP-Pro is unique in that it provides separate models for predicting CYP inhibitors vs. substrates, prioritizes high positive predictive value (PPV) as a pragmatic metric of accuracy to support the experimental testing of a small number of predicted substrates and inhibitors, enhances interpretability with fragment maps, and ensures reliability through strict applicability domain control. We expect that this new tool will aid researchers in early identification of compounds with favorable metabolic profiles, reducing the risks of drug-drug interactions and improving the efficiency of drug development efforts.