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Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump.
McLoughlin, Kevin S; Jeong, Claire G; Sweitzer, Thomas D; Minnich, Amanda J; Tse, Margaret J; Bennion, Brian J; Allen, Jonathan E; Calad-Thomson, Stacie; Rush, Thomas S; Brase, James M.
Afiliação
  • McLoughlin KS; Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94501, United States.
  • Jeong CG; GlaxoSmithKline, LLC 1250 S Collegeville Rd, Collegeville, Pennsylvania 19426, United States.
  • Sweitzer TD; GlaxoSmithKline, LLC 1250 S Collegeville Rd, Collegeville, Pennsylvania 19426, United States.
  • Minnich AJ; Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94501, United States.
  • Tse MJ; GlaxoSmithKline, LLC 1250 S Collegeville Rd, Collegeville, Pennsylvania 19426, United States.
  • Bennion BJ; Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94501, United States.
  • Allen JE; Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94501, United States.
  • Calad-Thomson S; GlaxoSmithKline, LLC 1250 S Collegeville Rd, Collegeville, Pennsylvania 19426, United States.
  • Rush TS; GlaxoSmithKline, LLC 1250 S Collegeville Rd, Collegeville, Pennsylvania 19426, United States.
  • Brase JM; Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94501, United States.
J Chem Inf Model ; 61(2): 587-602, 2021 02 22.
Article em En | MEDLINE | ID: mdl-33502191
ABSTRACT
Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Colestase / Doença Hepática Induzida por Substâncias e Drogas Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Colestase / Doença Hepática Induzida por Substâncias e Drogas Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos