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• W2891085910 abstract "Abstract 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." @default.
• W2891085910 created "2018-09-27" @default.
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• W2891085910 date "2018-10-02" @default.
• W2891085910 modified "2023-10-14" @default.
• W2891085910 title "MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes" @default.
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• W2891085910 doi "https://doi.org/10.1002/cmdc.201800309" @default.
• W2891085910 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30184341" @default.
• W2891085910 hasPublicationYear "2018" @default.
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