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NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

preprint
submitted on 28.08.2020 and posted on 31.08.2020 by Hyunwoo kim, Mingxun Wang, Christopher Leber, Louis-Felix Nothias, Raphael Reher, Kyo Bin Kang, Justin J. J. van der Hooft, Pieter Dorrestein, William Gerwick, Garrison Cottrell

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NP) research. Consequently, demands for automated NP classification system for massive data are increasing. The semantic ontology of NPs classifies molecules based on the taxonomy of the producing organism, the nature of the biosynthetic pathway, their biological properties, as well as the presence of chemical substructures. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs. Here, we introduce NPClassifier, the first deep-learning tool for the automated structural classification of NPs. We expect that NPClassifier will accelerate NP discovery by facilitating and enabling large-scale genome and metabolome mining efforts and linking of NP structures to their underlying bioactivity.

Funding

Tools for rapid and accurate structure elucidation of natural products

National Institute of General Medical Sciences

Find out more...

Gordon and Betty Moore Foundation grant (GBMF7622) to P.C.D., G.W.C., and W.H.G.

Netherlands eScience Center (NLeSC) ASDI eScience grant (ASDI.2017.030) to J.J.J.v.d.H.

History

Email Address of Submitting Author

gary@ucsd.edu

Institution

University of California, San Diego

Country

United States

ORCID For Submitting Author

0000-0001-7538-1715

Declaration of Conflict of Interest

William Gerwick and Garrison W. Cottrell are founders of NMR Finder, Inc.

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