用多智能体系统设计能靶向无序蛋白的生物药，效率提升超50%。
Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins
- 采用竞赛式推理框架，多智能体协作探索复杂构象空间。
- 对Der f 21设计787个候选物，超半数优于人工参考结合剂。
- 适用于高通量靶向无序蛋白药物发现，适合生物医药研发团队。
内在无序蛋白（IDPs）因在疾病中关键作用而成为重要治疗靶点——约80%的癌症相关蛋白含有长无序区段——但其缺乏稳定二级/三级结构，导致难以成药。尽管近期计算进展（如扩散模型）可设计高亲和力的IDP结合剂，但将其转化为实际药物发现仍需具备跨复杂构象集合进行自主推理，并规模化协调多种计算工具的系统。为此，我们设计并实现了StructBioReasoner，一个用于靶向IDPs的可扩展多智能体系统。该系统采用新型锦标赛式推理框架，使专业化智能体竞争生成与优化治疗假设，自然分配计算负载以高效探索巨大设计空间。智能体融合领域知识，接入文献综述、AI结构预测、分子模拟及稳定性分析，并通过可扩展的联邦智能体中间件Academy在高性能计算平台协调执行。我们在Der f 21和NMNAT-2上进行了基准测试，结果显示，在787个经验证的Der f 21候选物中，超过50%的结合自由能优于文献中的人工设计参考结合剂；对于更具挑战性的NMNAT-2，从97,066个结合剂中识别出三种结合模式，包括已知的NMNAT2:p53界面。因此，StructBioReasoner为在百亿亿级算力平台上构建面向IDP治疗发现的智能体推理系统奠定了基础。
原文摘要 · Abstract (English)
Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80\% of cancer-related proteins contain long disordered regions -- but their lack of stable secondary/tertiary structures makes them "undruggable". While recent computational advances, such as diffusion models, can design high-affinity IDP binders, translating these to practical drug discovery requires autonomous systems capable of reasoning across complex conformational ensembles and orchestrating diverse computational tools at scale.To address this challenge, we designed and implemented StructBioReasoner, a scalable multi-agent system for designing biologics that can be used to target IDPs. StructBioReasoner employs a novel tournament-based reasoning framework where specialized agents compete to generate and refine therapeutic hypotheses, naturally distributing computational load for efficient exploration of the vast design space. Agents integrate domain knowledge with access to literature synthesis, AI-structure prediction, molecular simulations, and stability analysis, coordinating their execution on HPC infrastructure via an extensible federated agentic middleware, Academy. We benchmark StructBioReasoner across Der f 21 and NMNAT-2 and demonstrate that over 50\% of 787 designed and validated candidates for Der f 21 outperformed the human-designed reference binders from literature, in terms of improved binding free energy. For the more challenging NMNAT-2 protein, we identified three binding modes from 97,066 binders, including the well-studied NMNAT2:p53 interface. Thus, StructBioReasoner lays the groundwork for agentic reasoning systems for IDP therapeutic discovery on Exascale platforms.
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