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FAME 3: Predicting the sites of metabolism in synthetic compounds and natural products for phase 1 and phase 2 metabolic enzymes

Sicho, Martin; Stork, Conrad; Mazzolari, Angelica; de Bruyn Kops, Christina; Pedretti, Alessandro; Testa, Bernard; Vistoli, Giulio; Svozil, Daniel; Kirchmair, Johannes
Journal article, Peer reviewed
Accepted version
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https://hdl.handle.net/11250/2738719
Utgivelsesdato
2019
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Samlinger
  • Department of Chemistry [335]
  • Registrations from Cristin [5233]
Originalversjon
Journal of Chemical Information and Modeling. 2019, 59 (8), 3400–3412.   10.1021/acs.jcim.9b00376
Sammendrag
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, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database (Pedretti et al. J. Med. Chem. 2018, 61, 1019), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox 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 via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.
Utgiver
American Chemical Society
Tidsskrift
Journal of Chemical Information and Modeling
Opphavsrett
Copyright 2019 American Chemical Society.

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