标题： 通过代理人工智能加速药物发现：在DMTA周期中实验室自动化的多代理方法 显示英文标题

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

摘要： 制药行业在药物发现方面面临前所未有的挑战，传统的方法难以满足现代治疗开发的需求。 本文介绍了一种新的人工智能框架Tippy，它通过在设计-制作-测试-分析（DMTA）循环中运行的专业人工智能代理来改变实验室自动化。 我们的多代理系统采用了五个专业代理——监督者、分子、实验室、分析和报告，在安全防护措施的监督下——每个代理都设计用于药物发现流程中的特定阶段。 Tippy是专门人工智能代理用于自动化DMTA循环的第一个可投入生产的实现，提供了一个人工智能如何改变实验室工作流程的具体示例。 通过利用能够推理、规划和协作的自主人工智能代理，我们展示了Tippy如何加速DMTA循环，同时保持对药物研究至关重要的科学严谨性。 该系统在工作流程效率、决策速度和跨学科协调方面表现出显著改进，为人工智能辅助的药物发现提供了一个新范式。 显示英文摘要

摘要： 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.