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Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods.
Gajewicz-Skretna, Agnieszka; Furuhama, Ayako; Yamamoto, Hiroshi; Suzuki, Noriyuki.
  • Gajewicz-Skretna A; Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland. Electronic address: agnieszka.gajewicz@ug.edu.pl.
  • Furuhama A; Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, 305-8506, Japan; Division of Genetics and Mutagenesis, National Institute of Health Sciences (NIHS), 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki City, Kanagawa, 210-9501, Japan
  • Yamamoto H; Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, 305-8506, Japan.
  • Suzuki N; Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, 305-8506, Japan.
Chemosphere ; 280: 130681, 2021 Oct.
Article en En | MEDLINE | ID: mdl-34162070
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Contaminantes Químicos del Agua / Relación Estructura-Actividad Cuantitativa Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Año: 2021 Tipo del documento: Article
Texto completo
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Contaminantes Químicos del Agua / Relación Estructura-Actividad Cuantitativa Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Año: 2021 Tipo del documento: Article