Evidence map›Paper›PMID 34662515›Full record

ArticleJournal of natural products2021

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

Hyun Woo Kim, Mingxun Wang, Christopher A Leber, Louis-Félix Nothias, Raphael Reher, Kyo Bin Kang, Justin J J van der Hooft, Pieter C Dorrestein, William H Gerwick, Garrison W Cottrell

Abstract read
In one paragraph

Article in Journal of natural products, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 276 papers.

0numbers the graph read from it
0cells of the map it votes in
276citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

276 citing papers in PubMed.

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  9. Biopesticidal Potential of theInternational journal of molecular sciences · 2026
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216 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Hyun Woo KimCenter for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0003-2473-8360
Mingxun WangSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0001-7647-6097
Christopher A LeberCenter for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States.
Louis-Félix NothiasSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States.
Raphael ReherCenter for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0002-5858-1173
Kyo Bin KangResearch Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women's University, Seoul 04310, Korea.ORCID 0000-0003-3290-1017
Justin J J van der HooftBioinformatics Group, Wageningen University, Wageningen 6700, The Netherlands.ORCID 0000-0002-9340-5511
Pieter C DorresteinSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0002-3003-1030
William H GerwickCenter for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0003-1403-4458
Garrison W CottrellDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093, United States.ORCID 0000-0001-7538-1715

Funding

Unified Computation Tools for Natural Products ResearchR01GM107550 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI COTTRELL, GARRISON W, GERWICK, WILLIAM HENRY · 2013 to 2025
$8.2M
NIGMS NIH HHS R01 GM107550
6 · The paper itself

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.

Indexed as

Neural Networks, ComputerBiological ProductsBiosynthetic PathwaysBiological Products

Identifiers

PMID34662515
PMCPMC8631337

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.