<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Moreira-Filho JT</submitter><funding>NIEHS NIH HHS</funding><pagination>101</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11330086</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>16(1)</volume><pubmed_abstract>With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of </pubmed_abstract><journal>Journal of cheminformatics</journal><pubmed_title>Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.</pubmed_title><pmcid>PMC11330086</pmcid><funding_grant_id>ES103376-02</funding_grant_id><pubmed_authors>Ranganath D</pubmed_authors><pubmed_authors>Mansouri K</pubmed_authors><pubmed_authors>Moreira-Filho JT</pubmed_authors><pubmed_authors>Conway M</pubmed_authors><pubmed_authors>Kleinstreuer N</pubmed_authors><pubmed_authors>Schmitt C</pubmed_authors></additional><is_claimable>false</is_claimable><name>Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.</name><description>With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of </description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Aug</publication><modification>2025-04-21T22:08:25.401Z</modification><creation>2025-04-05T18:38:04.184Z</creation></dates><accession>S-EPMC11330086</accession><cross_references><pubmed>39152469</pubmed><doi>10.1186/s13321-024-00894-1</doi></cross_references></HashMap>