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Democratising real-world drug discovery through agentic AI.

Jiazhen He1, Helen Lai2, Lakshidaa Saigiridharan1

  • 1Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, 431 83 Mölndal, Sweden.

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Agentic systems powered by large language models (LLMs) are now aiding drug discovery. ChatInvent, an LLM-based system, is integrated into AstraZeneca's pipeline for molecular design and synthesis planning.

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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.