Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle

• URL: http://arxiv.org/abs/2507.09023v1
• Date: Fri, 11 Jul 2025 21:13:13 GMT
• Title: Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle
• Authors: Yao Fehlis, Charles Crain, Aidan Jensen, Michael Watson, James Juhasz, Paul Mandel, Betty Liu, Shawn Mahon, Daren Wilson, Nick Lynch-Jonely, Ben Leedom, David Fuller,
• Abstract summary: This paper introduces a novel AI framework, Tippy, that transforms laboratory automation through specialized AI agents.<n>Our multi-agent system employs five specialized agents - Supervisor, Molecule, Lab, Analysis, and Report, with Safety Guardrail oversight.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The pharmaceutical industry faces unprecedented challenges in drug discovery, with traditional approaches struggling to meet modern therapeutic development demands. This paper introduces a novel AI framework, Tippy, that transforms laboratory automation through specialized AI agents operating within the Design-Make-Test-Analyze (DMTA) cycle. Our multi-agent system employs five specialized agents - Supervisor, Molecule, Lab, Analysis, and Report, with Safety Guardrail oversight - each designed to excel in specific phases of the drug discovery pipeline. Tippy represents the first production-ready implementation of specialized AI agents for automating the DMTA cycle, providing a concrete example of how AI can transform laboratory workflows. By leveraging autonomous AI agents that reason, plan, and collaborate, we demonstrate how Tippy accelerates DMTA cycles while maintaining scientific rigor essential for pharmaceutical research. The system shows significant improvements in workflow efficiency, decision-making speed, and cross-disciplinary coordination, offering a new paradigm for AI-assisted drug discovery.

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