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MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes
Author(s) -
Finkelmann Arndt R.,
Goldmann Daria,
Schneider Gisbert,
Göller Andreas H.
Publication year - 2018
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201800309
Subject(s) - cheminformatics , representation (politics) , computer science , workflow , phase (matter) , chemistry , biological system , machine learning , computational chemistry , combinatorial chemistry , artificial intelligence , organic chemistry , biology , database , politics , political science , law
The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in‐house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum‐chemistry‐derived molecular representations for reactivity prediction.