{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Karamertzanis PG"],"funding":["Intramural EPA"],"pagination":["600-619"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11258607"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["37(4)"],"pubmed_abstract":["Regulatory authorities aim to organize substances into groups to facilitate prioritization within hazard and risk assessment processes. Often, such chemical groupings are not explicitly defined by structural rules or physicochemical property information. This is largely due to how these groupings are developed, namely, a manual expert curation process, which in turn makes updating and refining groupings, as new substances are evaluated, a practical challenge. Herein, machine learning methods were leveraged to build models that could preliminarily assign substances to predefined groups. A set of 86 groupings containing 2,184 substances as published on the European Chemicals Agency (ECHA) website were mapped to the U.S. Environmental Protection Agency (EPA) Distributed Toxicity Structure Dat"],"journal":["Chemical research in toxicology"],"pubmed_title":["Systematic Approaches for the Encoding of Chemical Groups: A Case Study."],"pmcid":["PMC11258607"],"funding_grant_id":["EPA999999"],"pubmed_authors":["Patlewicz G","Sannicola M","Shah I","Paul-Friedman K","Karamertzanis PG"],"additional_accession":[]},"is_claimable":false,"name":"Systematic Approaches for the Encoding of Chemical Groups: A Case Study.","description":"Regulatory authorities aim to organize substances into groups to facilitate prioritization within hazard and risk assessment processes. Often, such chemical groupings are not explicitly defined by structural rules or physicochemical property information. This is largely due to how these groupings are developed, namely, a manual expert curation process, which in turn makes updating and refining groupings, as new substances are evaluated, a practical challenge. Herein, machine learning methods were leveraged to build models that could preliminarily assign substances to predefined groups. A set of 86 groupings containing 2,184 substances as published on the European Chemicals Agency (ECHA) website were mapped to the U.S. Environmental Protection Agency (EPA) Distributed Toxicity Structure Dat","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2025-07-07T03:10:14.89Z","creation":"2025-07-07T03:10:14.89Z"},"accession":"S-EPMC11258607","cross_references":{"pubmed":["38498310"],"doi":["10.1021/acs.chemrestox.3c00411"]}}