用AI代理自动完成药物研发全流程，提速且保科学性。
Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cycle
- 五类专用AI代理协同工作，覆盖设计-合成-测试-分析全流程
- 实现自动化流程加速，提升决策速度与跨学科协作效率
- 首个可投入生产的智能药物研发系统，适合制药与AI交叉研究者
制药行业在药物发现方面面临前所未有的挑战，传统方法难以满足现代治疗研发需求。本文提出Tippy框架，通过在设计-合成-测试-分析（DMTA）循环中部署专业化AI代理，实现实验室自动化。该多代理系统包含五类专用代理：监督者、分子、实验、分析和报告，并由安全防护机制监管。Tippy是首个可投入生产的专用AI代理系统，用于自动化DMTA循环，展示了自主AI代理如何通过推理、规划与协作，显著提升工作流效率、决策速度与跨学科协调能力，为人工智能辅助药物发现提供新范式。
原文摘要 · Abstract (English)
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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