Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise.

Journal: Computers in biology and medicine
Published Date:

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.

Authors

  • Vladimir Makarov
    Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, 173003 Veliky Novgorod, Russia.
  • Christophe Chabbert
    Roche Innovation Center Zurich, Switzerland.
  • Elina Koletou
    Roche Innovation Center Basel, Switzerland.
  • Fotis Psomopoulos
    Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki 570 01, Greece.
  • Natalja Kurbatova
    Data Infrastructure & Tools, Data Science & Artificial Intelligence, R&D, AstraZeneca, Cambridge, UK. natalie.kurbatova@astrazeneca.com.
  • Samuel Ramirez
    Eurofins, USA.
  • Chas Nelson
    Fjelltopp, UK.
  • Prashant Natarajan
    H2O.ai, UK.
  • Bikalpa Neupane
    Takeda, USA.