AI agents in drug discovery: applications and case studies

Area of Science:

• Biomedical research
• Artificial intelligence
• Drug discovery

Background:

• AI agents, powered by large language models and specialized tools, are increasingly capable of autonomous reasoning and action within scientific workflows.
• These agents can integrate diverse biomedical data, execute complex tasks, and conduct experiments, marking a significant advancement in computational biology.

Purpose of the Study:

• To provide a conceptual overview of agentic AI architectures.
• To illustrate the applications of AI agents across the drug discovery pipeline.
• To discuss challenges and future directions for AI in scientific research and translation.

Main Methods:

• Conceptual analysis of agentic AI architectures.
• Illustration of AI agent applications in drug discovery stages.
• Discussion of implementation challenges and future research avenues.

Main Results:

• AI agents demonstrate potential for autonomous reasoning, action, and learning in drug discovery workflows.
• Applications span literature synthesis, protocol generation, toxicity prediction, synthesis, and drug repurposing.
• Early implementations show significant improvements in speed, reproducibility, and scalability.

Conclusions:

• Agentic AI offers transformative potential for accelerating and enhancing drug discovery processes.
• Addressing challenges in data, reliability, privacy, and benchmarking is crucial for widespread adoption.
• Future development of AI technologies will further support scientific advancement and clinical translation.