Artificial intelligence in drug development

Area of Science:

• Pharmacology and Pharmaceutical Sciences
• Computational Biology and Bioinformatics
• Artificial Intelligence in Medicine

Background:

• Traditional drug development is lengthy, costly, and relies heavily on empirical methods.
• Emerging artificial intelligence (AI) technologies, including large language models (LLMs) and generative AI, offer transformative potential.
• AI integration is already yielding improvements in drug development efficiency and effectiveness.

Purpose of the Study:

• To provide a comprehensive overview of recent AI advancements in drug development.
• To examine AI applications across the entire drug development pipeline.
• To identify challenges and future research directions for AI-augmented drug development.

Main Methods:

• Review of recent literature on AI applications in drug development.
• Analysis of AI integration across key stages: target identification, drug discovery, preclinical studies, clinical trials, and post-market surveillance.
• Critical examination of current challenges and future research opportunities.

Main Results:

• AI demonstrates significant potential to enhance efficiency and effectiveness in drug discovery and development.
• AI applications span the entire workflow, from initial target identification to post-market surveillance.
• Subtle yet meaningful enhancements have already been observed with AI integration.

Conclusions:

• AI, particularly LLMs and generative AI, is revolutionizing the drug development paradigm.
• Addressing current challenges is crucial for unlocking the full potential of AI in pharmaceutical innovation.
• Future research should focus on further integrating and optimizing AI tools for accelerated and improved drug development.