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Prediction of Drug Metabolites Using Neural Machine Translation

preprint
submitted on 19.05.2020 and posted on 20.05.2020 by Eleni Litsa, Payel Das, Lydia Kavraki
We present an end-to-end learning-based method for predicting possible human metabolites of small molecules including drugs. The metabolite prediction task is approached as a sequence translation problem with chemical compounds represented using the SMILES notation. We perform transfer leaning on a Seq2Seq Transformer model originally trained on chemical reaction data to predict the outcome of human metabolic reactions. We further build an ensemble model to account for multiple and diverse metabolites.
Extensive evaluation reveals that the proposed method generalizes well to different enzyme families, as it can correctly predict metabolites for phase I and phase II drug metabolism reactions as well as for other enzymes.

Funding

Rice University

CPRIT RP170508

History

Email Address of Submitting Author

Eleni.Litsa@rice.edu

Institution

Rice University

Country

USA

ORCID For Submitting Author

0000-0002-4746-8706

Declaration of Conflict of Interest

None

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