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Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond.

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  • 1Global Health, Integrated Development, Bill & Melinda Gates Foundation, Seattle, Washington, USA;

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Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) accelerate early drug discovery but face challenges in lead optimization. Developing AI/ML tools for lead-to-candidate decisions is crucial for efficient pharmaceutical development.

Keywords:
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Area of Science:

  • Pharmaceutical research and development
  • Computational chemistry
  • Drug discovery

Background:

  • AI and ML are widely used in early-stage pharmaceutical research, including target identification and compound library generation.
  • Progress in applying AI/ML to lead optimization and lead-to-candidate (L2C) decision-making, particularly for ADMET properties, has been slower.
  • Existing AI/ML applications focus on research phases like hit identification and library optimization.

Purpose of the Study:

  • To review the challenges and progress of AI/ML in lead optimization and L2C decision-making.
  • To identify reasons for slower AI/ML adoption in later drug development stages.
  • To summarize remaining issues and the importance of AI/ML for derisking pharmaceutical development.

Main Methods:

  • Literature review of AI/ML applications in pharmaceutical R&D.
  • Analysis of progress and challenges in applying AI/ML to lead optimization.
  • Survey of AI/ML tools for predicting ADMET properties.

Main Results:

  • AI/ML has been extensively applied to early drug discovery stages.
  • Slower progress is observed in using AI/ML for predicting absorption, distribution, metabolism, excretion, and toxicology (ADMET) properties.
  • Recent years have seen advancements, but significant issues remain in AI/ML for L2C decisions.

Conclusions:

  • Effective AI/ML tools are vital for derisking L2C and later pharmaceutical development phases.
  • Accelerating drug development and reducing costs depend on advancing AI/ML in lead optimization.
  • Greater success rates in drug development can be achieved by overcoming current AI/ML limitations.