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Machine learning applications in drug development.

Clémence Réda1,2, Emilie Kaufmann3, Andrée Delahaye-Duriez1,4,5

  • 1NeuroDiderot, UMR 1141, Inserm, Université de Paris, Sorbonne Paris Cité, Hôpital Robert Debré, 48, boulevard Sérurier, Paris 75019, France.

Computational and Structural Biotechnology Journal
|January 25, 2021
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Summary

Automated drug development pipelines using machine learning can accelerate discovery and improve disease understanding. This survey explores sequential learning and recommender systems for efficient pharmaceutical research.

Keywords:
Adaptive clinical trialBayesian optimizationCollaborative filteringDrug discoveryDrug repurposingMulti-armed bandit

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Area of Science:

  • Biomedical data science
  • Computational drug discovery
  • Machine learning in medicine

Background:

  • Vast amounts of biological and medical data are available.
  • Machine learning algorithms are well-established.
  • Pharmaceutical companies face low productivity rates in drug development.

Purpose of the Study:

  • To envision largely automated drug development pipelines.
  • To guide and accelerate drug discovery processes.
  • To enhance understanding of diseases and biological phenomena.

Main Methods:

  • Focus on sequential learning methods.
  • Focus on recommender systems.
  • Leveraging machine learning for automation.

Main Results:

  • Automation can significantly speed up drug discovery.
  • Improved understanding of diseases and biological mechanisms.
  • Aids in planning preclinical and clinical trials.

Conclusions:

  • Automated drug development pipelines are feasible.
  • Sequential learning and recommender systems are key methods.
  • Automation can address the productivity challenge in pharmaceuticals.