Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors.

Wilm, Anke; Garcia de Lomana, Marina; Stork, Conrad; Mathai, Neann; Hirte, Steffen; Norinder, Ulf; Kühnl, Jochen; Kirchmair, Johannes

Wilm, Anke; Garcia de Lomana, Marina; Stork, Conrad; Mathai, Neann; Hirte, Steffen; Norinder, Ulf; Kühnl, Jochen; Kirchmair, Johannes.

Afiliação

Wilm A; Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany.
Garcia de Lomana M; HITeC e.V., 22527 Hamburg, Germany.
Stork C; Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria.
Mathai N; Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany.
Hirte S; Computational Biology Unit (CBU), Department of Chemistry, University of Bergen, N-5020 Bergen, Norway.
Norinder U; Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria.
Kühnl J; MTM Research Centre, School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden.
Kirchmair J; Department of Computer and Systems Sciences, Stockholm University, SE-16407 Kista, Sweden.

Pharmaceuticals (Basel) ; 14(8)2021 Aug 11.

Article em En | MEDLINE | ID: mdl-34451887

Palavras-chave

bioactivity descriptors; conformal prediction; in silico prediction; machine learning; random forest; skin sensitization; toxicity prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Pharmaceuticals (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Alemanha País de publicação: Suíça

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