{"doi":"10.1016/j.scitotenv.2024.174201","title":"Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions","abstract":null,"journal":"Science of The Total Environment","year":2024,"id":644333,"datarank":0.868817675428532,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"self_citation_contribution":0.5244761342199721,"citation_network_contribution":0.34434154120856,"self_endowment_contribution":0.5244761342199721,"citer_contribution":0.34434154120856,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"citer_count":32,"citers_with_citation_signal":22,"citers_with_endowment":22,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1677000,"name":"Beihai Zhou","orcid":null,"position":1,"is_corresponding":false},{"id":1212583,"name":"Haijun Liu","orcid":"0000-0002-2320-1425","position":2,"is_corresponding":false},{"id":1677001,"name":"Yuefang Chen","orcid":null,"position":3,"is_corresponding":false},{"id":1677002,"name":"Rongfang Yuan","orcid":null,"position":4,"is_corresponding":false},{"id":1677003,"name":"Zhongbing Chen","orcid":null,"position":5,"is_corresponding":false},{"id":1318475,"name":"Shuai Luo","orcid":null,"position":6,"is_corresponding":false},{"id":1677004,"name":"Huilun Chen","orcid":null,"position":7,"is_corresponding":false},{"id":1676999,"name":"Lingxuan Meng","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions","abstract":"Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.","is_dataset_classified":null,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38936709","pmcid":null,"openalex_id":"https://openalex.org/W4399992915","authors":[],"funders":[{"funder_name":"Fundamental Research Funds for the Central Universities","grant_id":"FRF-EYIT-23-07","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"42377361","title":null},{"funder_name":"Anqing Normal University","grant_id":"","title":null}],"total_grants":3,"fwci":5.3752,"citation_percentile":0.96912659,"influential_citations":0,"citation_trend":[{"year":2024,"count":2},{"year":2025,"count":22},{"year":2026,"count":8}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-004","oa_locations":[{"url":"https://api.elsevier.com/content/article/PII:S0048969724043493?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0048969724043493?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.scitotenv.2024.174201","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38936709","host_type":"repository"}],"fields_of_study":["Per- and polyfluoroalkyl substances research","Machine Learning","Fluorocarbons","Quantitative Structure-Activity Relationship","Environmental Pollutants","Toxicity Tests","Humans","Ecotoxicology"],"mesh_terms":["Machine Learning","Environmental Pollutants","Fluorocarbons","Humans","Toxicity Tests","Quantitative Structure-Activity Relationship","Ecotoxicology"],"keywords":["Toxicity","Environmental toxicology","Adverse Outcome Pathway","Computational biology","Computer science","Biochemical engineering","Environmental chemistry","Chemistry","Biology","Engineering","Toxicity evaluation","Toxicity prediction","Machine Learning","Pfass"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Zero hunger"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T00:55:01.692432Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}