COCONUT online: Collection of Open Natural Products database

By Maria Sorokina, Peter Merseburger, Kohulan Rajan, Mehmet Aziz Yirik, Christoph Steinbeck. Journal of Cheminformatics, 2020

523 citations recorded by the source corpus.

Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other in silico applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at https://coconut.naturalproducts.net .

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/403320206608290d12b8bce52c52f948ca21adb0

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Citations in the corpus

Artificial Intelligence For Natural Product Drug Discovery (2023, background): “For example, the NP Atlas 170,171 , COCONUT 202 and LOTUS 203 databases provide information about chemical structures, and the MIBiG database contains information on BGCs 169 .”
The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods (2023, methodology): “The Natural Product (NP) flag was revised for release 33 to use a newer implementation based on mappings to COCONUT, an open-source collection of NPs ( 36 ).”
ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance & Efficiency on a Specific Domain (2024, unknown): “The data sources used are PubChem [36], English Wikipedia, BeIR [37], CoconutDB [38], and Safety Data Sheets [39] each offering unique and complementary information critical for evaluating the performance of NLP models in chemistry.”