Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision

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.