Predicting the binding of small molecules to nuclear receptors using machine learning.

Ramaprasad, Azhagiya Singam Ettayapuram; Smith, Martyn T; McCoy, David; Hubbard, Alan E; La Merrill, Michele A; Durkin, Kathleen A

Ramaprasad, Azhagiya Singam Ettayapuram; Smith, Martyn T; McCoy, David; Hubbard, Alan E; La Merrill, Michele A; Durkin, Kathleen A.

Affiliation

Ramaprasad ASE; Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, CA 94720, USA.
Smith MT; Divisions of Environmental Health Sciences and Biostatistics, School of Public Health, University of California Berkeley, CA 94720, USA.
McCoy D; Divisions of Environmental Health Sciences and Biostatistics, School of Public Health, University of California Berkeley, CA 94720, USA.
Hubbard AE; Divisions of Environmental Health Sciences and Biostatistics, School of Public Health, University of California Berkeley, CA 94720, USA.
La Merrill MA; Department of Environmental Toxicology, University of California, Davis, CA 95616, USA.
Durkin KA; Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, CA 94720, USA.

Brief Bioinform ; 23(3)2022 05 13.

Article in En | MEDLINE | ID: mdl-35383362

Subject(s)
Key words

Machine learning; Nuclear Receptor; Super Learner; Toxicity

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Receptors, Cytoplasmic and Nuclear / Endocrine Disruptors Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Brief Bioinform Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2022 Document type: Article Affiliation country: Estados Unidos

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