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Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise.
Makarov, Vladimir; Chabbert, Christophe; Koletou, Elina; Psomopoulos, Fotis; Kurbatova, Natalja; Ramirez, Samuel; Nelson, Chas; Natarajan, Prashant; Neupane, Bikalpa.
Afiliação
  • Makarov V; The Pistoia Alliance, 401 Edgewater Place, Suite 600, Wakefield, MA, 01880, USA. Electronic address: vladimir.makarov@pistoiaalliance.org.
  • Chabbert C; Roche Innovation Center Zurich, Switzerland.
  • Koletou E; Roche Innovation Center Basel, Switzerland.
  • Psomopoulos F; Centre for Research & Technology Hellas (CERTH), Greece.
  • Kurbatova N; Zifo R&D, UK.
  • Ramirez S; Eurofins, USA.
  • Nelson C; Fjelltopp, UK.
  • Natarajan P; H2O.ai, UK.
  • Neupane B; Takeda, USA.
Comput Biol Med ; 177: 108632, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38788373
ABSTRACT
Machine Learning (ML) and Artificial Intelligence (AI) have become an integral part of the drug discovery and development value chain. Many teams in the pharmaceutical industry nevertheless report the challenges associated with the timely, cost effective and meaningful delivery of ML and AI powered solutions for their scientists. We sought to better understand what these challenges were and how to overcome them by performing an industry wide assessment of the practices in AI and Machine Learning. Here we report results of the systematic business analysis of the personas in the modern pharmaceutical discovery enterprise in relation to their work with the AI and ML technologies. We identify 23 common business problems that individuals in these roles face when they encounter AI and ML technologies at work, and describe best practices (Good Machine Learning Practices) that address these issues.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Indústria Farmacêutica / Descoberta de Drogas / Aprendizado de Máquina Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Indústria Farmacêutica / Descoberta de Drogas / Aprendizado de Máquina Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article