Democratising real-world drug discovery through agentic AI

Area of Science:

• Artificial Intelligence in Chemistry
• Drug Discovery Technologies
• Computational Chemistry

Background:

• Agentic systems leveraging large language models (LLMs) show potential in chemical research.
• Existing systems like CoScientist, Chemcrow, and LLM-RDF demonstrate utility in cheminformatics and reaction development.
• Real-world adoption of these systems in drug discovery pipelines remains underexplored.

Purpose of the Study:

• To present a real-world example of an agentic system's adoption in drug discovery.
• To detail the development and integration of the ChatInvent system at AstraZeneca.
• To discuss the evolution, challenges, and future perspectives of agentic systems in pharmaceutical research.

Main Methods:

• Development of an agentic system named ChatInvent, based on large language models.
• Integration of ChatInvent into AstraZeneca's drug discovery pipeline.
• Evolution from a single-agent proof-of-concept to a multi-agent architecture with a graphical user interface.

Main Results:

• Successful integration of the ChatInvent agentic system into a major pharmaceutical company's discovery pipeline.
• Demonstration of the system's utility in molecular design and synthesis planning.
• Establishment of an extensible, robust, and scalable multi-agent architecture.

Conclusions:

• Agentic systems represent a significant advancement in AI-driven drug discovery.
• The ChatInvent project provides a practical model for implementing LLM-based tools in pharmaceutical research.
• Continued development and addressing persistent challenges are crucial for realizing the full potential of agentic systems in chemistry.