FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes

Abstract

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, drug-like compounds, natural products, agrochemicals and cosmetics. FAME 3 was derived from the MetaQSAR database (J Med Chem 2018, 61, 1019-1030), a recently published data resource on xenobiotic metabolism that contains more than 2,100 substrates annotated with more than 6,300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other non-redox reactions, and conjugation reactions. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure ( FAMEscore ) that is based on a nearest neighbor search in the space of atom environments. FAME 3 is available as a self-contained Java software package, free for academic and non-commercial research.