Anthropic wants to develop its own drugs

Anthropic 想要自主研发药物

The AI drug boom has a long way to go before reaching patients. 人工智能药物研发热潮距离真正惠及患者还有很长的路要走。

At the event “The Briefing: AI for Science” earlier this week, Anthropic announced Claude Science, a new “AI workbench for scientists” that pulls fragmented tools and datasets into one environment, and generates figures and visuals. Anthropic, already dominating the industry with its popular coding tools and powerful AI models, framed the launch around what it says is AI’s potential to “dramatically accelerate the pace of scientific discovery and the development of healthcare interventions,” and touted a long list of biotech and pharma customers already using Claude. 在本周早些时候举行的“简报：科学人工智能”（The Briefing: AI for Science）活动上，Anthropic 发布了 Claude Science。这是一个全新的“科学家 AI 工作台”，旨在将碎片化的工具和数据集整合到一个环境中，并能生成图表和视觉内容。Anthropic 凭借其广受欢迎的编程工具和强大的 AI 模型已在行业中占据主导地位。该公司将此次发布定位为 AI 能够“显著加快科学发现和医疗干预开发步伐”的潜力体现，并宣称已有众多生物技术和制药领域的客户正在使用 Claude。

Anthropic also went a step further, saying it would develop drugs of its own. Head of life sciences Eric Kauderer-Abrams said the company will focus on discovering treatments for “neglected” diseases. Anthropic 还更进一步，表示将自主研发药物。其生命科学负责人 Eric Kauderer-Abrams 表示，公司将专注于发现针对“被忽视”疾病的治疗方法。

AI companies have been eager to court science and pharma customers — OpenAI, Amazon, Google, and others have their own life sciences tools and platforms. But Anthropic’s planned move is one of the most direct public attempts by a major frontier AI company to actually develop drugs itself. It puts it in the unusual position of selling software to other, potentially competing drugmakers. Anthropic joins a broader race that includes AI-first drug companies like Insilico, Google DeepMind spinout Isomorphic Labs, biotech startups, and Big Pharma companies building or buying AI tools of their own. AI 公司一直热衷于争取科学和制药领域的客户——OpenAI、亚马逊、谷歌等公司都有自己的生命科学工具和平台。但 Anthropic 的这一计划是主流前沿 AI 公司首次如此直接地公开尝试自主研发药物。这使其处于一种不同寻常的境地：既向其他潜在的竞争对手制药商出售软件，又参与到一场更广泛的竞赛中，这场竞赛的参与者还包括 Insilico 等 AI 原生药物公司、谷歌 DeepMind 分拆出的 Isomorphic Labs、生物技术初创公司以及正在自建或购买 AI 工具的大型制药公司。

Anthropic has provided very few specific details about what it hopes to accomplish in the drug development space. At the event, Kauderer-Abrams didn’t say what the company would do if it finds any promising drug candidates. Anthropic did not respond to The Verge’s requests for comment seeking more details, including what diseases it plans to target first and whether it would partner up with other companies for lab work, animal testing, clinical trials, or manufacturing. Anthropic 对于其希望在药物研发领域实现的目标提供的具体细节非常有限。在活动中，Kauderer-Abrams 并未说明如果发现有前景的候选药物，公司将采取什么行动。Anthropic 没有回应《The Verge》关于寻求更多细节的置评请求，包括其计划优先针对哪些疾病，以及是否会与其他公司合作进行实验室工作、动物实验、临床试验或生产制造。

Experts told The Verge that the uncertainty surrounding Anthropic’s plans reflects a broader uncertainty around the AI drug boom itself. “AI drug discovery” can mean many things. It “is a really broad term,” explained Namshik Han, a professor at the University of Cambridge and cofounder of AI biotech startup CardiaTec. AI is applied at “every single stage of drug discovery,” he said, from finding new compounds and improving them to supporting research, data analysis, clinical trials, and even manufacturing. Every major drug company will be using AI in some way, he said. Matthew Todd, a professor of drug discovery at University College London, echoed the sentiment that AI already pervades drug discovery and research, calling it a “catchall phrase” given its broad array of uses. 专家告诉《The Verge》，围绕 Anthropic 计划的不确定性反映了 AI 药物研发热潮本身更广泛的不确定性。“AI 药物发现”可以指代很多事情。剑桥大学教授、AI 生物技术初创公司 CardiaTec 的联合创始人 Namshik Han 解释说，这是一个“非常宽泛的术语”。他说，AI 被应用于“药物发现的每一个阶段”，从寻找和改进新化合物，到支持研究、数据分析、临床试验，甚至是生产制造。他表示，每家大型制药公司都会以某种方式使用 AI。伦敦大学学院药物发现教授 Matthew Todd 也表达了同样的观点，认为 AI 已经渗透到药物发现和研究中，鉴于其广泛的用途，他将其称为一个“包罗万象的短语”。

AI is undoubtedly changing drug development. Han pointed to the numerous initiatives by pharma giants like AstraZeneca, Novo Nordisk, and GSK, and said AI can already help generate possible drug ideas, such as by suggesting new molecules that could interact with parts of the body like cell receptors that are already known to be involved with a particular disease or are targets of existing drugs. Todd said it’s immensely useful for speeding up research and helping “road test” new drug ideas. Given Anthropic’s work on frontier models, the company would presumably use generative AI to search across vast chemical and biological possibilities and help researchers make connections that would be difficult or slow to find otherwise, potentially suggesting new drug ideas, identifying new disease targets, or finding new uses for existing drugs. AI 无疑正在改变药物开发。Han 指出，阿斯利康（AstraZeneca）、诺和诺德（Novo Nordisk）和葛兰素史克（GSK）等制药巨头已经开展了大量举措。他表示，AI 已经可以帮助生成可能的药物构思，例如通过建议新的分子，这些分子可以与人体中已知与特定疾病有关或作为现有药物靶点的部分（如细胞受体）相互作用。Todd 表示，这对于加速研究和帮助“路测”新药物构思非常有用。鉴于 Anthropic 在前沿模型方面的工作，该公司可能会利用生成式 AI 在广阔的化学和生物可能性中进行搜索，帮助研究人员建立原本难以发现或发现缓慢的联系，从而可能提出新的药物构思、识别新的疾病靶点或发现现有药物的新用途。

But that is still a long way from an AI-designed drug reaching patients. Todd said the field is “a long way off” from an AI-designed drug being approved by regulators for human use. He added that the drug discovery process would not run autonomously, with human input and supervision required throughout. Todd and Han both noted the lack of publicly available, high-quality experimental data, such as how various chemicals behave in the body, could slow drug development efforts as well, stressing that even for well-studied areas of biology there are still large gaps in our understanding of how things work. 但这距离 AI 设计的药物惠及患者还有很长的路要走。Todd 表示，距离 AI 设计的药物获得监管机构批准用于人体，该领域还有“很长的路要走”。他补充说，药物发现过程不会自动运行，整个过程都需要人类的投入和监督。Todd 和 Han 都指出，缺乏公开的高质量实验数据（例如各种化学物质在体内的表现）也可能拖慢药物研发进程，并强调即使在生物学研究充分的领域，我们对事物运作方式的理解仍存在巨大差距。

Frank von Delft, a professor of structural chemical biology at the University of Oxford and head of protein crystallography at the Oxford Centre for Medicines Discovery, said people are right to get excited about advancing AI models, but they “haven’t yet come close to making experiments unnecessary.” Drug candidates still have to be tested in the real world for efficacy, toxicity, and whether they have practical properties allowing them to be prepared, stored, and delivered safely as medicines. All of that requires skilled workers, a lot of money, and time, especially clinical work in humans — a point when many promising drug candidates fail. If Anthropic wants to develop a drug, von Delft said, it is “going to have to spend a lot on experiments.” 牛津大学结构化学生物学教授、牛津药物发现中心蛋白质晶体学负责人 Frank von Delft 表示，人们对 AI 模型的进步感到兴奋是正确的，但它们“还远未达到让实验变得多余的地步”。候选药物仍必须在现实世界中进行功效、毒性测试，并验证其是否具备作为药物进行制备、储存和安全输送的实际特性。所有这些都需要熟练的工人、大量的资金和时间，尤其是人体临床试验——这是许多有前景的候选药物失败的阶段。von Delft 说，如果 Anthropic 想要开发一种药物，它“将不得不在实验上投入巨资”。

It’s possible Anthropic is willing to try. In the last year, the company has been actively hiring biologists and building its own wet labs, and as of writing it has several live applications hiring for life sciences roles. Anthropic 可能愿意尝试。在过去的一年里，该公司一直在积极招聘生物学家并建立自己的湿实验室。截至撰稿时，该公司仍有多个生命科学职位的招聘信息处于开放状态。