Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms.
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| Title: | Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms. |
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| Authors: | Daood NJ; Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States., Russo DP; Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States., Chung E; Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States.; Center for Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States., Qin X; Tulane National Primate Research Center, Tulane University School of Medicine, Covington, Louisiana 70433, United States., Zhu H; Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States.; Center for Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States. |
| Source: | Environment & health (Washington, D.C.) [Environ Health (Wash)] 2024 May 28; Vol. 2 (7), pp. 474-485. Date of Electronic Publication: 2024 May 28 (Print Publication: 2024). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 9918646069906676 Publication Model: eCollection Cited Medium: Internet ISSN: 2833-8278 (Electronic) Linking ISSN: 28338278 NLM ISO Abbreviation: Environ Health (Wash) Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 2833-8278 |
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| DOI: | 10.1021/envhealth.4c00026 |
