<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Gaudry A</submitter><funding>Ministerie van Buitenlandse Zaken</funding><funding>Schweizerischer Nationalfonds zur F??rderung der Wissenschaftlichen Forschung</funding><funding>Swiss National Science Foundation</funding><funding>Government of the United Kingdom</funding><funding>Swiss Open Research Data Grants</funding><funding>Bundesministerium f??r Bildung und Forschung</funding><pagination>494-510</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10979503</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>10(3)</volume><pubmed_abstract>The ENPKG framework organizes large heterogeneous metabolomics data sets as a knowledge graph, offering exciting opportunities for drug discovery and chemodiversity characterization.</pubmed_abstract><journal>ACS central science</journal><pubmed_title>A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery.</pubmed_title><pmcid>PMC10979503</pmcid><funding_grant_id>164095</funding_grant_id><funding_grant_id>CRSII5_189921/1</funding_grant_id><funding_grant_id>316030</funding_grant_id><funding_grant_id>189921</funding_grant_id><pubmed_authors>Mehl F</pubmed_authors><pubmed_authors>Ioset JR</pubmed_authors><pubmed_authors>Quiros-Guerrero LM</pubmed_authors><pubmed_authors>Pagni M</pubmed_authors><pubmed_authors>Queiroz EF</pubmed_authors><pubmed_authors>Cappelletti L</pubmed_authors><pubmed_authors>David B</pubmed_authors><pubmed_authors>Allard PM</pubmed_authors><pubmed_authors>Moretti S</pubmed_authors><pubmed_authors>Wolfender JL</pubmed_authors><pubmed_authors>Gaudry A</pubmed_authors><pubmed_authors>Grondin A</pubmed_authors><pubmed_authors>Marcourt L</pubmed_authors><pubmed_authors>Rutz A</pubmed_authors><pubmed_authors>Kaiser M</pubmed_authors></additional><is_claimable>false</is_claimable><name>A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery.</name><description>The ENPKG framework organizes large heterogeneous metabolomics data sets as a knowledge graph, offering exciting opportunities for drug discovery and chemodiversity characterization.</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Mar</publication><modification>2026-05-29T12:03:18.035Z</modification><creation>2025-04-06T07:46:37.76Z</creation></dates><accession>S-EPMC10979503</accession><cross_references><pubmed>38559298</pubmed><doi>10.1021/acscentsci.3c00800</doi></cross_references></HashMap>