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NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
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Abstract
Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.
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“CANOPUS166 utilizes these predicted fingerprints, which are esoteric to chemists, to predict the natural product class167 of the unknown natural product.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [167]
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Self-citation
Metadata
- Authors
- Kim, H. W., Wang, M., Leber, C. A., Nothias, L.-F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W.
- Affiliations
- Scripps Institution of Oceanography (US)
- University of California San Diego (US)
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Sookmyung Women's University (KR)
- Bioinformatics Group, Wageningen University, Wageningen 6700, The Netherlands
- Corresponding author
- Gerwick, William H.
- Cottrell, Garrison W.
- ORCID
- Venue
- Journal of Natural Products
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-10-18
- Volume
- 84
- Issue
- 11
- Pages
- 2795-2807
- ISSN
- 0163-3864 (ISSN-L)
- 1520-6025
- PMC
- PMC8631337
- OpenAlex
- W3206742358
- Cited by
- 551
- References
- 70
- Citations by year
- 2026: 140 · 2025: 165 · 2024: 115 · 2023: 83 · 2022: 30 · 2021: 18
- FWCI
- 32.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Natural Products (publishedVersion, cc-by-nc-nd)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
- Funders
- Gordon and Betty Moore Foundation (GBMF7622)
- National Research Foundation of Korea (NRF-2020R1C1C1004046)
- Netherlands eScience Center (ASDI.2017.030)
- National Institute of General Medical Sciences (R01 GM107550)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Microbial Natural Products and Biosynthesis
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- natural products, structural classification, Morgan fingerprints, deep neural network, natural product discovery, biosynthetic pathway, chemical substructures
- MeSH terms
- Biological Products, Neural Networks, Computer, Biosynthetic Pathways
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