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Uncertainty quantification in drug design.

Lewis H Mervin1, Simon Johansson2, Elizaveta Semenova3

  • 1Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.

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Summary
This summary is machine-generated.

Machine learning enhances drug design by generating molecules and predicting properties. Quantifying prediction uncertainty is crucial for autonomous decision-making in drug discovery.

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly used in drug design due to algorithmic advancements, data availability, and computational power.
  • Progress has been made in molecular generation, synthesis prediction, and property prediction, but often without quantifying prediction uncertainty.

Purpose of the Study:

  • To review uncertainty quantification (UQ) methods in ML for drug design.
  • To highlight the importance of UQ for autonomous decision-making and integrated drug discovery cycles.

Main Methods:

  • Review of empirical, frequentist, and Bayesian approaches to UQ.
  • Discussion of UQ's application in drug design processes.

Main Results:

  • UQ is essential for reliable ML-driven drug design.
  • UQ enables better integration of ML with chemistry automation for a design-make-test-analyze cycle.

Conclusions:

  • Uncertainty quantification is critical for advancing autonomous drug design.
  • Implementing UQ will enhance decision-making in the drug discovery pipeline.