全球搜索：面向药物资产勘探的宽搜索AI代理，用于投资、业务开发和竞争情报
Hunt Globally: Wide Search AI Agents for Drug Asset Scouting in Investing, Business Development, and Competitive Intelligence
AI总结：
本文提出一种基于树结构的自学习Bioptic代理，用于在多语言、异构数据源中实现高召回率的药物资产勘探，通过构建多代理流水线和基准测试，验证了其在非美国中心雷达数据中的完整性和准确性。
摘要：
生物制药创新已发生转变：许多新药资产现在源于美国以外地区，并主要通过区域、非英语渠道披露。最新数据显示，超过85%的专利申请来自美国以外，中国占全球总量的近一半；越来越多的学术产出也非美国起源。行业估计，中国占全球药物开发的30%，涵盖1200多个新型候选药物。在高风险环境下，未能发现“隐蔽”资产将给投资者和业务开发团队带来数十亿美元的风险，使资产勘探成为关键覆盖的竞争领域，速度和完整性驱动价值。然而，目前的深度研究AI代理在异构、多语言数据源中实现高召回率发现方面仍逊于人类专家。我们提出了一种用于药物资产勘探的基准方法和一种经过调优的树状自学习Bioptic代理，旨在实现完整、无幻觉的勘探。我们构建了一个具有挑战性的完整性基准，使用多语言多代理流水线：复杂用户查询配对真实资产，这些资产大多在以美国为中心的雷达之外。为了反映真实的交易复杂性，我们收集了来自专家投资者、业务开发和风投专业人士的筛选查询，并将其作为先验条件，有条件地生成基准查询。在评分方面，我们使用LLM作为裁判评估，校准到专家意见。在该基准上，我们的Bioptic代理实现了79.7%的F1分数，优于Gemini 3.1 Deep Think（59.2%）、Gemini 3.1 Pro Deep Research（58.6%）、Claude Opus 4.6（56.2%）、OpenAI GPT-5.2 Pro（46.6%）、Perplexity Deep Research（44.2%）和Exa Websets（26.9%）。随着额外计算资源的增加，性能显著提升，支持了更多计算资源带来更好结果的观点。
英文摘要：
Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United States and are disclosed primarily via regional, non-English channels. Recent data suggests over 85% of patent filings originate outside the U.S., with China accounting for nearly half of the global total; a growing share of scholarly output is also non-U.S. Industry estimates put China at 30% of global drug development, spanning 1,200+ novel candidates. In this high-stakes environment, failing to surface "under-the-radar" assets creates multi-billion-dollar risk for investors and business development teams, making asset scouting a coverage-critical competition where speed and completeness drive value. Yet today's Deep Research AI agents still lag human experts in achieving high-recall discovery across heterogeneous, multilingual sources without hallucinations. We propose a benchmarking methodology for drug asset scouting and a tuned, tree-based self-learning Bioptic Agent aimed at complete, non-hallucinated scouting. We construct a challenging completeness benchmark using a multilingual multi-agent pipeline: complex user queries paired with ground-truth assets that are largely outside U.S.-centric radar. To reflect real deal complexity, we collected screening queries from expert investors, BD, and VC professionals, and used them as priors to conditionally generate benchmark queries. For grading, we use LLM-as-judge evaluation calibrated to expert opinions. On this benchmark, our Bioptic Agent achieves 79.7% F1 score, outperforming Gemini 3.1 Deep Think (59.2%), Gemini 3.1 Pro Deep Research (58.6%), Claude Opus 4.6 (56.2%), OpenAI GPT-5.2 Pro (46.6%), Perplexity Deep Research (44.2%), and Exa Websets (26.9%). Performance improves steeply with additional compute, supporting the view that more compute yields better results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
