title: Quantitative Prediction of Regioselectivity Toward Cytochrome P450/3A4 Using Machine Learning Approaches.
abstract: In the drug discovery process, it is important to know the properties of both drug candidates and their metabolites. Fast and precise prediction of metabolites is essential. However, it has been difficult to predict metabolites because of the complexity of the mechanism of cytochrome P450/3A4 (CYP 3A4), which is the main metabolite enzyme of drugs. In this study, we focus on the regioselectivity of CYP 3A4, i.e., the selectivity of metabolic sites. We have developed a model to predict the regioselectivity of drug candidates by using machine learning (ML) approaches.
authors.affiliation: Chugai Pharmaceutical Company, Kamakura Research, Laboratories, Kajiwara 200, Kamakura, Kanagawa 247-8530, Japan.
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Hasegawa
authors.affiliation: Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan phone: (+81) 03-5841-7751 fax: (+81) 03-5841-7771.
authors.forename: Michio
authors.initials: M
authors.lastname: Koyama
authors.affiliation: Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan phone: (+81) 03-5841-7751 fax: (+81) 03-5841-7771. funatsu@chemsys.t.u-tokyo.ac.jp.
authors.forename: Kimito
authors.initials: K
authors.lastname: Funatsu
keywords: ADMET; CYP 3A4; Random forest; Regioselectivity; Structure-activity relationships