Therapeutic discovery remains a formidable challenge, impeded by the fragmentation of specialized domains and the execution gap between computational design and physiological validation. Although generative AI offers promise, current models often function as passive assistants rather than as autonomous executors. Here, we introduce OrchestRA, a human-in-the-loop multi-agent platform that unifies biology, chemistry, and pharmacology into an autonomous discovery engine. Unlike static code generators, our agents actively execute simulations and reason the results to drive iterative optimization. Governed by an Orchestrator, a Biologist Agent leverages deep reasoning over a massive knowledge graph (>10 million associations) to pinpoint high-confidence targets; a Chemist Agent autonomously detects structural pockets for de novo design or drug repositioning; and a Pharmacologist Agent evaluates candidates via rigorous physiologically based pharmacokinetic (PBPK) simulations. This architecture establishes a dynamic feedback loop where pharmacokinetic and toxicity profiles directly trigger structural reoptimization. By seamlessly integrating autonomous execution with human guidance, OrchestRA democratizes therapeutic design, transforming drug discovery from a stochastic search to a programmable evidence-based engineering discipline.

翻译：治疗发现仍然是一项艰巨的挑战，其阻碍因素包括专业领域的碎片化以及计算设计与生理验证之间的执行鸿沟。尽管生成式人工智能展现出前景，但当前模型通常仅作为被动助手而非自主执行器。本文介绍OrchestRA，一种人在回路的多智能体平台，它将生物学、化学和药理学统一为自主发现引擎。与静态代码生成器不同，我们的智能体主动执行模拟并对结果进行推理，以驱动迭代优化。在协调器的统筹下，生物学家智能体通过对海量知识图谱（超过1000万关联）的深度推理来锁定高置信度靶点；化学家智能体自主识别结构空腔以进行从头设计或药物重定位；药理学家智能体则通过严格的基于生理的药代动力学（PBPK）模拟评估候选药物。该架构建立了一个动态反馈循环，其中药代动力学和毒性特征可直接触发结构再优化。通过将自主执行与人类指导无缝集成，OrchestRA实现了治疗设计的民主化，将药物发现从随机搜索转变为可编程的循证工程学科。