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Machine Learning in Drug Discovery and Development Part 1: A Primer.
Talevi, Alan; Morales, Juan Francisco; Hather, Gregory; Podichetty, Jagdeep T; Kim, Sarah; Bloomingdale, Peter C; Kim, Samuel; Burton, Jackson; Brown, Joshua D; Winterstein, Almut G; Schmidt, Stephan; White, Jensen Kael; Conrado, Daniela J.
Afiliação
  • Talevi A; Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), Buenos Aires, Argentina.
  • Morales JF; Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), Buenos Aires, Argentina.
  • Hather G; Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA.
  • Podichetty JT; Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA.
  • Kim S; Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA.
  • Bloomingdale PC; Quantitative Pharmacology and Pharmacometrics, Merck & Co. Inc, Kenilworth, New Jersey, USA.
  • Kim S; Canary Speech LLC, Provo, Utah, USA.
  • Burton J; Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA.
  • Brown JD; Center for Drug Evaluation and Safety, Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
  • Winterstein AG; Center for Drug Evaluation and Safety, Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
  • Schmidt S; Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA.
  • White JK; Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA.
  • Conrado DJ; e-Quantify LLC, La Jolla, California, USA.
CPT Pharmacometrics Syst Pharmacol ; 9(3): 129-142, 2020 03.
Article em En | MEDLINE | ID: mdl-31905263
ABSTRACT
Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Descoberta de Drogas / Aprendizado de Máquina / Desenvolvimento de Medicamentos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Descoberta de Drogas / Aprendizado de Máquina / Desenvolvimento de Medicamentos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article