礼来CEO对话英伟达黄仁勋：AI制药的下一个战场是“数据产能”？_老虎社区_美港股上老虎

礼来CEO对话英伟达黄仁勋：AI制药的下一个战场是“数据产能”？

E药经理人01-14

当全球市值最高的制药企业与全球市值最高的芯片公司，在全球医药创新风向标的顶级峰会上携手合作，背后释放了怎样的行业信号？在最近开幕的第44届摩根大通医疗健康年会（J.P. Morgan Healthcare Conference）上，英伟达CEO黄仁勋与礼来CEO Dave Ricks的一场跨界对话，清晰勾勒出AI制药的发展目标与未来路径。图片来源：英伟达官网具体而言，礼来和英伟达将在未来五年内投资...

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Morgan Healthcare Conference）上，英伟达CEO黄仁勋与礼来CEO Dave Ricks的一场跨界对话，清晰勾勒出AI制药的发展目标与未来路径。图片来源：英伟达官网具体而言，礼来和英伟达将在未来五年内投资高达10亿美元，用于人才、基础设施和计算资源，共同成立首个人工智能联合创新实验室。关于合作的目标，用英伟达黄仁勋的表述更加直白：英伟达与礼来的合作旨在打造一套全新的药物发现蓝图，研发人员可以在合成一个分子之前，就先用计算机模拟探索海量的生物和化学可能性。”因此，这场强强联合真正释放的信号，恰恰不是“AI将很快打破双十定律”。相反，两位掌门人用更务实的路径告诉行业：AI短期最确定的价值仍在早期发现端，让数据成为第一生产力，让干湿实验（计算模拟和实验室实操）形成连续学习闭环，用高质量数据持续生成、持续迭代，把药物发现推向更高效、更可复制、更可规模化的工程体系。01数据站上C位这次合作最值得反复咀嚼的细节，并不是礼来或是英伟达这两家全球最为知名的AI领域大买家又买了什么，而是在双方发布的新闻稿中被反复强调的一句话：双方将并肩合作，去“生成大规模数据并构建强大AI模型”。这句话直接点破了AI制药竞争的“主战场”迁移：过去，AI制药领域竞争的核心是模型、算法、框架；接下来，AI制药竞争的C位将是数据，数据的产能与质量，决定未来应用潜力的上限。原因显而易见：新药研发不同于互联网行业。在互联网中，数据天然存在，用户行为、社交图谱、交易记录……每时每刻都在自动生成。而在药物研发中，最关键的数据——靶点与分子的相互作用、结构与活性的关联、化学空间的合成可行性、蛋白质的真实动态构象，都并非存储于硬盘之中，而是蕴藏在实验里，必须依靠资金、时间、人力、物力，才能将其生产出来。所以双方的对话反复提及一个关键词：海量、高质量的数据生成。换句话说，未来大模型竞争的本质不在模型，而在数据。谁拥有更强的数据生产能力，谁就拥有更强的模型迭代速度。这也是为什么英伟达与礼来不是一次简单的合作，而是在共同打造一个更接近未来制药业的基础设施：用实验体系持续产生高质量数据，再用英伟达的BioNeMo作为关键平台，持续训练生物化学基础模型，最终可以以模型反馈指导实验设计，形成数据生产的正向循环。在此逻辑下，数据被置于核心位置，甚至成为影响未来竞争力的关键因素：数据生产能力，即新药研发的“产能”。02构建药物研发的持续学习系统如果说将数据放在C位定义了未来竞争的核心因素，那么，礼来和英伟达对话的第二个关键信号则指向一条逐渐清晰的实践路径。在双方的对话中，将合作的第一阶段目标表述得极为明确：将着重构建一个具有持续学习能力的系统，将礼来的智能湿实验室与计算干实验室紧密连接，从而实现全天候人工智能辅助实验，为大分子药物和小分子药物的研发提供支持。这种运行模式旨在使实验、数据生成和人工智能模型开发能够相互促进、持续改进。事实上，过去AI制药往往面临着落地质疑，例如模型可生成分子，但生成之后如何验证？筛选出潜力候选分子，如何实证其可行性？实验结果显示巨大差异时，如何快速修正？在传统模式下，干、湿实验和人工智能三者相互脱节。AI没有成为连接和驱动干湿实验的核心，反而像一个孤立的外围工具，导致研发流程效率低下、迭代缓慢。而礼来与英伟达所合作的 “连续学习系统” ，正是为了解决这一问题，旨在将AI打造为贯穿和驱动整个研发闭环的引擎。将“实验-数据-模型-再实验”的闭环流程整合为一套可持续迭代的运转体系。就像礼来CEO Dave Ricks在对话中所言：“机器的价值，就在于夜以继日地运转，攻克这一难题。”这一观点也揭示了一个核心趋势：AI制药领域的下一轮竞争，焦点将不再局限于算法、模型的优劣，而是转向全流程、持续性、稳定性与高效性的综合实力比拼，其本质，正是打造一条永不停歇的AI研发“流水线”。当然，全球芯片巨头与制药龙头在AI制药领域的携手，的确为AI制药的发展描绘出了一个清晰的方向——成为驱动“干湿实验闭环”的核心引擎。其雄心在于，将早期药物发现从依赖灵感的探索，重塑为可计算、可迭代的工业化体系，从而让研发的起点更高，失败的成本更低。然而，双方合作的宏大蓝图也清晰地揭示了当前AI能力的边界。即使是最先进的合作，也未能回答行业最根本的问题：如何用AI真正突破药物研发的“双十定律”？它的主战场，依然聚焦于早期发现阶段。而对于“双十定律”中更具决定性的、也是风险与成本最为集中的临床阶段，复杂的人体生物学、疾病机理的未知性、个体化差异带来的不确定性，AI仍是一个有限的参与者，而非颠覆者。当全球最顶尖的巨头联手，也仅能将答案推进至此，这本身便是一个强烈的信号：AI制药的工业化时代已拉开序幕，但攻克“双十定律”的终极战役，依然道阻且长。这要求每一位参与者，既怀抱技术带来的希望，也保持对科学本质的清醒认知。当礼来与英伟达的对话已为行业指明方向，AI正从研发“外挂”转变为驱动“连续学习系统”的核心引擎，数据生产能力成为竞争制高点，我们究竟该如何跟进？1月27日，“2026医药钉峰会暨E药经理人首届AI大会”将在上海举办。本次大会将集结本土头部Pharma、顶尖Biotech、科技平台与投资机构的一线决策者，共同追问AI在新药研发中的真实价值与落地瓶颈。我们期待与您一同穿透概念，回归场景，共同探寻一条属于中国医药产业的AI工业化之路。诚邀您莅临上海，与行业同行者一起，展开这场关乎未来十年的关键对话。扫描二维码报名参会扫描加入大会交流群一审| 石宛佳二审| 李芳晨三审| 李静芝","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"hasMoreComments":false,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"isCrawlerRequest":true}