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Accelerating drug discovery, development, and clinical trials by artificial intelligence.

Yilun Zhang1, Mohamed Mastouri2, Yang Zhang2

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Artificial intelligence (AI) is revolutionizing drug development for small molecules, RNA, and antibodies. Adopting advanced AI models like large language models can overcome hurdles to AI-designed drug approvals.

Keywords:
RNATranslation to patientsantibodyartificial intelligenceclinical trialdeep learningdrug developmentsmall molecule

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Area of Science:

  • Biomedical research
  • Pharmaceutical sciences
  • Computational chemistry

Background:

  • Artificial intelligence (AI) significantly impacts biomedical research and drug discovery.
  • AI offers transformative potential for pharmaceutical innovation, particularly in developing small molecules, RNA therapeutics, and antibodies.

Purpose of the Study:

  • To provide a comprehensive analysis of AI-assisted drug development progress.
  • To examine AI integration within the industrial drug development framework.
  • To identify challenges and propose solutions for AI-conceived drug approvals.

Main Methods:

  • Review of AI applications in small molecule, RNA, and antibody drug development.
  • Analysis of the current industrial drug development process.
  • Examination of drugs currently in clinical trials.
  • Literature review on AI methodologies and their integration.

Main Results:

  • AI is making substantial progress in various aspects of drug development.
  • The integration of AI into industrial drug development is ongoing.
  • A key challenge remains the lack of AI-designed drugs receiving regulatory approval.

Conclusions:

  • AI holds significant potential to accelerate and enhance drug discovery and development.
  • Large language models and diffusion models are proposed as strategies to overcome approval barriers.
  • The field of AI in pharmaceuticals is dynamic, with ongoing challenges and promising prospects.