Bayesian Classification of Cytochrome P450 3A4 Substrates/Non-substrates and Color Mapping for Chemical Interpretation

Abstract

Prediction of cytochrome P450 (CYP) 3A4 substrates is valuable for finding promising drug candidates and a considerable amount of attention has been devoted to in silico predictions. Machine learning (ML) methods have recently been explored for perfoming ligand-based approaches. ML methods utilize supervised learning methods such as neural networks, support vector machines and Bayesian approaches to develop statistical models. In this paper, we used Bayesian approach to classify CYP 3A4 substrates and non-substrates. The extended connectivity fingerprint (ECFP) descriptor was used as chemical descriptor. The atom score was calculated from the Bayesian score of each fingerprint. By visualizing the atom scores with five graded-colors, the color mapping for each compound was performed. This can be used for chemical interpretaion why the specific compound exhibits CYP 3A4 substrate. The established Bayesian model and the associated color mapping would be useful for avoiding the risk of CYP 3A4 substrate in early drug discovery. The parallel use of the prediction of oxidation sites in the subsequent paper can give us de novo prediction of any molecules concerning CYP 3A4.