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Area of Science:

  • Drug discovery and development
  • Artificial intelligence in medicine
  • Regulatory science

Background:

  • Drug research faces challenges including long timelines, high costs, and significant attrition rates.
  • Recent advancements in artificial intelligence (AI) offer potential solutions to these challenges.
  • Global regulatory frameworks are evolving to accommodate AI-driven innovations.

Purpose of the Study:

  • To review recent AI advancements in drug discovery and development.
  • To align AI innovations with global regulatory frameworks.
  • To explore how AI can enhance decision-making and reduce inefficiencies in drug R&D.

Main Methods:

  • Narrative review integrating recent AI advances across the drug development pipeline.
  • Analysis of AI applications in target identification, drug repurposing, molecular design, structural biology, safety prediction, and clinical development.
  • Examination of AI integration with regulatory guidelines and new approach methodologies.

Main Results:

  • AI integration can shorten development timelines, improve compound quality, and increase early-phase success rates.
  • Predictive and interpretable AI, combined with mechanistic priors and external validation, enhances decision-making.
  • Case studies show AI-assisted discovery and repurposing benefits, but challenges like overfitting and dataset bias persist.

Conclusions:

  • AI holds significant promise for optimizing drug research and development.
  • Establishing credibility plans and ensuring equity-by-design are crucial for generalizable AI impact.
  • AI integration with new methodologies and adaptive trials can reduce inefficiency without compromising rigor.