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Related Experiment Videos

Automating drug discovery.

Gisbert Schneider1

  • 1Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.

Nature Reviews. Drug Discovery
|December 16, 2017
PubMed
Summary
This summary is machine-generated.

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Automated drug discovery using microfluidics and AI accelerates compound optimization. This review explores robust technologies and challenges for medicinal chemists to adopt these innovations.

Area of Science:

  • Medicinal Chemistry
  • Drug Discovery Automation
  • Chemical Biology

Background:

  • Small-molecule drug discovery is a complex, multidimensional optimization problem balancing efficacy, pharmacokinetics, and safety.
  • Recent technological advancements offer potential for increased automation in drug discovery processes.

Purpose of the Study:

  • To identify robust technologies and approaches for medicinal chemists in the near future.
  • To critically analyze the opportunities and challenges of widespread automation in drug discovery.

Main Methods:

  • Review of recent advances in microfluidics-assisted synthesis and biological testing.
  • Analysis of artificial intelligence (AI) systems for feedback-driven hypothesis improvement.
  • Critical evaluation of conceptual, technical, and organizational challenges.

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Main Results:

  • Automation via microfluidics and AI can accelerate compound discovery and optimization.
  • These technologies enable more effective exploration of chemical space.
  • Significant challenges and skepticism surround the current hype of these automated approaches.

Conclusions:

  • Medicinal chemists can implement specific automated approaches robustly in the near future.
  • Widespread application requires addressing considerable conceptual, technical, and organizational hurdles.
  • A balanced perspective is needed to navigate the opportunities and challenges of drug discovery automation.