Capecchi A, Reymond JL. Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning.
J Cheminform 2021;
13:82. [PMID:
34663470 PMCID:
PMC8524952 DOI:
10.1186/s13321-021-00559-3]
[Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/02/2021] [Indexed: 01/13/2023] Open
Abstract
Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin ( https://tm.gdb.tools/map4/coconut_tmap/ ), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance ( https://np-svm-map4.gdb.tools/ ). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.
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