Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-based Label Space Division Method

A drug may be metabolized by multiple cytochrome P450 (CYP450) isoforms. Predicting the metabolic fate of drugs is very important to prevent drug–drug interactions in the development of novel pharmaceuticals. Prediction of CYP450 enzyme–substrate selectivity is formulized as a multilabel learning task in this study. First, we compared the performance of feature combinations based on four different categories of features, which are physiochemical property descriptors, mol2vec descriptors, extended connectivity fingerprints, and molecular access system key fingerprints on modeling. After identifying the best combination of features, we applied seven different multilabel models, which are multilabel k-nearest neighbor (ML-kNN), multilabel twin support vector machine, and five network-based label space division (NLSD)-based methods (NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM). All of the six models (ML-kNN, NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM) in this paper exhibit better performances...