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Enhancing preclinical drug discovery with artificial intelligence.

R S K Vijayan1, Jan Kihlberg2, Jason B Cross1

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Area of Science:

  • Pharmacology
  • Biotechnology
  • Computer Science

Background:

  • Artificial intelligence (AI) is increasingly integrated into the pharmaceutical industry.
  • AI offers potential across the entire drug discovery and development pipeline.
  • Current applications demonstrate AI's significant impact on preclinical research.

Purpose of the Study:

  • To review current artificial intelligence technologies in drug discovery.
  • To illustrate how AI is transforming preclinical drug discovery with real-world examples.
  • To provide a balanced view of AI adoption, including opportunities and challenges.

Main Methods:

  • Literature review of AI technologies and applications in drug discovery.
  • Analysis of case studies showcasing AI's impact on preclinical research.
  • Discussion of the practicalities of implementing AI in pharmaceutical R&D.

Main Results:

  • AI technologies are actively reshaping various stages of drug discovery and development.
  • Specific examples highlight AI's successful application in preclinical research.
  • Significant opportunities exist for AI to enhance efficiency and success rates.

Conclusions:

  • AI presents a realistic pathway to advance drug discovery and development.
  • Addressing challenges is crucial for successful AI integration.
  • AI is poised to become a cornerstone of future pharmaceutical innovation.