Anthropic生命科学团队努力加快治愈疾病。 在过去几个月里，我们的科学家与六个开发新药物的组织建立了深入的研究伙伴关系：BigHat Biosciences、Bristol Myers Squibb、Divigence Labs、Enveda、Generate Biochologys和The College Group。 我们还开始自己的毒品调查工作。 Claude如何支持药物研发，我们计划从这里大幅度扩大这项工作。
有许多机会可以加速药物的开发，甚至超越AI最常讨论的应用，比如提出新分子的初步设计。 开发一种药物涉及许多不同的任务：进行早期研究以确定一种药物的靶子和与之接触的正确药物类型，设计一个具有正确特性的分子，优化其生产，使其具有可靠规模，起草进行临床试验所需的规章备案，设计和管理这些试验，等等。
我们实现AI医疗利益的战略是基于AI越来越多地参与所有这些任务。 没有单一的问题可以解决；瓶颈在整个药物开发过程中都分布。 找出这些瓶颈并开展工作的最佳方法是深入参与真正的药物方案，涉及许多不同的疾病领域和发展阶段。
因此，在过去几个月里，我们与6个组织建立了合作关系。 Claude负责大部分科学和业务工作。
这项工作以三种方式服务于我们的目标。 首先，它让我们更好地了解我们模式在更广泛的药物开发任务方面的长处和局限性(我们已经根据我们迄今学到的知识，将具体改进模式列为优先事项)。 第二，这些方案可作为更广泛的生命科学界的案例研究。 如果Claude要大幅提高新疗法的治愈率，我们需要与更多的生物技术公司合作，将Claude更深入地融入它们的发展进程。 第三，如果这些项目的正轨继续下去，它们本身应产生直接影响。 我们乐观地认为，有些药物可能会为它们所针对的疾病带来有效的新药，包括食物过敏、罕见的儿童疾病和几种癌症。
在此文中，我们介绍我们与合作伙伴的最新进展，以及技术报告和预印本，更详细地描述我们的思想进程、方法和早期成果。 然后，我们简要概述我们下一步的工作方向。
个案研究
生成生物医学：模式工程。 生成生物医学一直致力于提高癌症治疗的效用，称为CAR T细胞疗法。 这种疗法历来有助于治疗血细胞癌；生成旨在重塑它，以便它也能治疗固体肿瘤癌。 要做到这一点，他们的目标是缩短治疗的遗传指导的一部分，这将为适合其他癌症的额外指导创造空间。 六位生成科学家使用Claude Code为这一部分提出新的设计。 一个设计比标准版本短约70%，在实验室测试中效果更好：它允许免疫细胞制造大约10倍于受体的数量(它们用来检测癌细胞的蛋白质)，它更高效地杀死肿瘤细胞，免疫细胞保持活动时间更长。 更多信息，请查看Generate的技术报告。
差异实验室：命中生成。 差异实验室正在研究一种治疗食物过敏的新方式。 食物过敏症是由一种叫做IgE的分子的反应引发的，有些反应附着在免疫细胞上，有些反应在血液中自由漂浮。 不同的实验室正在研发药物，将IgE从免疫细胞中完全分离出来，而不是像标准药物那样，完全中和在血液中循环的IgE。 这可以提供一种更为有效的治疗方法，但它需要一种精确的设计，有两种“武器”，一种与IgE紧密结合，另一种有助于从免疫细胞中喷出IgE。 Claude将Divence Labs的目标转化为一个详细的设计计划，然后在大约一个月的时间里，基本上自主地产生了大约35,000个抗体设计，以缩小范围并进行测试。 在这些早期的试验中，Dividence发现了两只手臂的“达到”：三种可以与IgE结合的设计，20种可以将其拆卸。 这20种药物中，有3种似乎比标准药物更能驱散IgE。 它的下一步是建立一个结合两臂的药物。 更多信息，请查看 Divergence的技术报告。
BigHat生物科学：铅优化。 BigHat Biosciences将AI与自动实验室配对，以改善潜在药物(或“铅”)，这些药物必须紧密地绑定目标，通过制造和储存保持稳定，并避免挤合在一起(与其他标准一样)。 BigHat的科学家和Anthropic研究者与Claude合作，在两条线索中改进了这三种特性，这两条线索都可能成为未来药物的一部分的抗体碎片：一条旨在瞄准癌细胞，一条旨在将一种药物留在体内更长的时间。 Claude与BigHat的实验室数据系统Reccy相连接，提出了每个分子的改变，BigHat的实验室在大约10周内做了大约1,010个变体的测试。 在整个过程中，调查组怀疑标准热稳定性和凸起度量度具有误导性。 Claude和研究人员对原始数据进行了重新分析，然后提出了不同的度量衡和协议更改来测试它们。 BigHat的科学家们精炼并运行了它，主要预测得到了坚持。 与原版相比，最好的版本捆绑在10x以上更紧，癌靶铅留在溶液中高18°C，而耐久药物的铅在100x以上多吸 ly. 请参看BigHat的技术报告。
恩维达：制造与档案。 为了进行临床试验，公司需要记录表明每批新药都符合严格的质量标准，并向监管机构提交申请等要求。 在恩维达，制造化学家花费了75%的时间从事文书工作。 为了减轻负载，恩维达建立了一个系统，将所有制造文件连接到Claude，然后让Claude标出任何可能的问题。 Claude在1 061页的文件测试中，捕捉到所有11个人类评论员发现的问题，在这个过程中，这种方法将人的审查时间减少了89%，从25小时以上减少到不到3小时。 另外，Claude还撰写了两个重要监管文件的初稿。 一个用了四个小时，而手动大约三个月。 另一方面，恩韦达的标准人文审查程序发现一个小错误。 更多信息，请查看Enveda的技术报告。
列组(TCG)：公司创建。 由于药物的发现可能缓慢、昂贵和容易失败，生物技术公司的投资者倾向于更安全、事先得到验证的想法，而不是风险更大的新想法，即使后者可以帮助更多的病人。 我们一直与TCG合作，研究Claude是否能通过缩短药物发现的时间和成本来改变这一点，具体地说，TCG正在建立Pleiades公司，这是一家新的公司，它将使用Claude来经营比小团队通常更多的，更复杂的毒品方案。 这可以成为Claude如何减少经营生物技术公司所需的科学、业务和行政间接费用的模式。 如果这一模式可行，可以在整个行业推广，并将投资引向更严重的医学问题。 Pleiades目前正在为Claude开发新的工具和工作流程；它的第一个Claude技能帮助Claude评估药物目标是否值得追求，已经对45对目标-疾病对夫妇进行了评价。 详读。
最近，我们还开始与布里斯托尔·迈尔斯·斯基布(BMS)合作，寻找并解决临床试验规划中的瓶颈问题。 BMS正使用Claude来评估临床试验中早期药物的计划，并估计这些试验成功的可能性。 我们计划一起在2027年扩大这项工作，以了解它提高早期发展成功概率的潜力。
“我们在临床试验阶段使用Claude来分析专有数据，制定临床研究计划，并权衡研究的成功机会。 Claude从一个瞬间产生一个强有力的初步分析，帮助我们评估不同的发展选择，否则需要几天时间才能完成。 我们在确保高度敏感数据安全的同时做了所有这些工作。 我们期待着将这项工作扩展至整个管道，以支持组合层面的决定。”
迈克尔·波德赫纳德(Michael Pourdehnad,MD)，高级副总裁，早期临床发展，血液学，肿瘤学和细胞治疗部负责人。
除了我们与这些合作伙伴的合作之外，我们自己也开始运行一些研究项目。 我们拥有收购 Mithril Medicine，一家单一人的公司，其创始人科迪·特兰巴格(Cody Tranbarger)利用Claude与11个外部实验室组织合同，后勤，实验，并将Mithril业务的许多其他方面委托给Claude. 因此，Mithril在不到18个月的时间里对一种形式的肌肉萎缩进行了可能的治疗，从一个想法到对小鼠有希望的结果。 在Anthropic，科迪继续这项研究，并计划开始几个新的方案。 如果他的药物考生继续坚挺，我们期望一种药物能在2028年末尽快开始人类临床试验。 更多信息，请参见技术报告。
扩大我们在生命科学方面的工作
所有这些研究仍处于早期阶段。 特别是，到目前为止，它大多是临床前的，即大多数情况下，它还没有进入人类正在试验药物的阶段。 随着这些药物计划的进展，以及随着我们与更多药物开发计划已经进入临床阶段的组织的合作，我们计划继续将Claude的作用扩展到临床试验。
这项研究只是我们加速药物发现努力的一部分。 归根结底，我们希望与尽可能多的药物开发研究人员分享我们的模型。 为此，我们推出了Claude Science，我们的人工智能为研究人员工作，我们正在为生命科学家提供Claude学分。 我们还创立了生命科学核查计划，让受信任的组织能够使用我们最强大的模型，我们最近还公开呼吁生命科学的客座调查员；我们正在寻求研究团体的建议
我们期待与更多组织合作，如果你在一个组织工作专注于药物发现并且有兴趣与我们合作，你可以联系到下面。
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Anthropic’s life sciences team is working to accelerate curing disease. Over the past several months, our scientists have engaged in deep research partnerships with six organizations developing new medicines: BigHat Biosciences, Bristol Myers Squibb, Divergence Labs, Enveda, Generate Biomedicines, and The Column Group. We've also begun drug discovery efforts of our own. In doing so, we've learned a lot about how Claude can support drug development, and how we’ll need to improve Claude further to realize AI's potential in medicine. We plan to scale this work up substantially from here.
There are many opportunities to speed up drug development, even beyond the most commonly discussed applications of AI, like proposing initial designs for new molecules. Developing a drug involves many varied tasks: doing early research to identify a target for a drug and the right type of drug to engage it, designing a molecule with the right properties, optimizing it to be manufactured reliably at scale, drafting the regulatory filings needed to run clinical trials, designing and administering those trials, and much more.
Our strategy to realize AI’s medical benefits is based on having AI increasingly involved in all of these tasks. There is no single problem to solve; the bottlenecks are distributed all throughout the drug development process. The best way to identify and work through these bottlenecks is to get deeply involved in real drug programs, across many different disease areas and stages of development.
So, over the past several months, we’ve partnered with six organizations to do so. We’ve also started drug discovery efforts of our own, including programs for rare childhood forms of muscular dystrophy, where Claude handles much of the scientific and operational work.
This work serves our goals in three ways. First, it lets us better understand the strengths and limitations of our models in relation to a much wider range of drug development tasks (we’ve already prioritized specific improvements to our models based on what we’ve learned so far). Second, these programs can serve as case studies for the wider life sciences community. If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes. Third, these projects should, if their positive trajectories continue, have direct impact themselves. We’re optimistic that some may result in effective new medicines for the diseases they target, including food allergies, rare childhood diseases, and several forms of cancer.
In this post, we provide an update on our progress with our partners, along with technical reports and preprints that describe our thought process, methods, and early results in more detail. We then briefly outline where our work is headed next.
Case studies
Generate Biomedicines: modality engineering. Generate Biomedicines has been working on increasing the usefulness of a cancer treatment known as CAR T-cell therapy. This therapy has historically helped treat blood cell cancers; Generate aims to reengineer it so that it can treat solid tumor cancers too. To do this, they’re aiming to shorten one part of the therapy’s genetic instructions, which would create room for additional instructions suited to other kinds of cancer. Six Generate scientists used Claude Code to propose new designs for this part. One design was about 70% shorter than the standard version, and in lab tests it was more effective: it allowed immune cells to make roughly 10 times as many receptors (the proteins they use to spot cancer cells), it killed tumor cells more efficiently, and the immune cells remained active for longer. Read more, and see Generate’s technical report.
Divergence Labs: hit generation. Divergence Labs is working on a novel way to treat food allergies. Food allergies are set off by a reaction from a molecule called IgE, some of which is attached to our immune cells and some of which floats freely in our blood. Divergence Labs is developing drugs that would detach the IgE from our immune cells entirely, rather than solely neutralizing the IgE circulating in our blood, as standard medications do. This could provide a much more effective treatment, but it requires a precise design, with two “arms”: one that would bind tightly to IgE, and another that would help prise IgE from the immune cell. Claude converted Divergence Labs’ goals into a detailed design plan, and then, largely autonomously, generated roughly 35,000 antibody designs in about a month to be narrowed down and tested. In those early tests, Divergence found “hits” for both arms: three designs that could bind to IgE, and 20 that could dislodge it. Three of those 20 appeared to dislodge IgE more than the standard medication. Its next step is to build a drug that combines the two arms. Read more, and see Divergence’s technical report.
BigHat Biosciences: lead optimization. BigHat Biosciences pairs AI with an automated lab to improve potential drugs (or “leads”), which must bind their target tightly, remain stable through manufacturing and storage, and avoid clumping together (among other criteria). BigHat’s scientists and an Anthropic researcher worked with Claude to improve these three properties in two leads, both antibody fragments that could become parts of future drugs: one designed to target cancer cells, and one designed to keep a drug in the body longer. Claude, connected to BigHat’s lab data system, Reccy, proposed changes to each molecule, and BigHat’s lab made and tested about 1,010 variants in about 10 weeks. Partway through, the team suspected the standard heat-stability and clumping metrics were misleading. Claude and the researcher reanalyzed the raw data, then proposed different metrics and a protocol change to test them. BigHat’s scientists refined and ran it, and the main predictions held. Compared with the originals, the best versions bound more than 10x more tightly, the cancer-targeting lead stayed in solution 18°C higher, and the lead for longer-lasting drugs clumped more than 100x more slowly. Read more, and see BigHat’s technical report.
Enveda: manufacturing and filings. To conduct clinical trials, companies need records showing that each batch of a new drug has been made to strict quality standards, and to submit an application to regulators, among other requirements. At Enveda, manufacturing chemists spent up to 75% of their time on paperwork. To ease the load, Enveda built a system that connects all of its manufacturing documents to Claude, then lets Claude flag any possible issues. Over tests on 1,061 pages of documentation, Claude caught all 11 issues that human reviewers had spotted—and flagged seven they’d missed. In the process, this method reduced human review time by 89%, from over 25 hours to less than three. Separately, Claude wrote the first drafts of two important regulatory documents. One took four hours, compared to about three months by hand. For the other, Enveda’s standard human review process found one minor error. Read more, and see Enveda’s technical report.
The Column Group (TCG): company creation. Because drug discovery can be slow, expensive, and prone to failure, investors in biotechnology companies have favored safer, previously validated ideas over riskier new ones, even when the latter could help more patients. We’ve been working with TCG to see whether Claude can change this by cutting the time and cost of drug discovery so that investors can confidently support more ambitious ideas. Specifically, TCG is establishing Pleiades, a new company that will use Claude to run many more—and more complex—drug programs than is usual for a small team. This could be a model for how Claude can reduce both the scientific and the operational and administrative overhead required to run a biotechnology company. If this works, this model could be replicated across the industry and direct investment toward harder problems in medicine. Pleiades is currently developing new tools and workflows for Claude; its very first Claude skill, which helps Claude assess whether drug targets are worth pursuing, has been evaluated on 45 target-disease pairs. Read more.
More recently, we’ve also begun working with Bristol Myers Squibb (BMS) to find and address bottlenecks in clinical trial planning. BMS is using Claude to assess plans of an early-stage drug in clinical trials, and to estimate the likelihood of those trials succeeding. Together, we plan to expand this work in 2027 to understand its potential for increasing the probability of success in early development.
“We use Claude at the clinical trial stage to analyze proprietary data, create a clinical study plan, and weigh the study’s chance of success. From a single prompt, Claude generated a robust initial analysis that helped inform our assessment of different development options, which would otherwise take days to complete. We did all of this while ensuring the security of our highly sensitive data. We look forward to expanding this work across our pipeline to support portfolio-level decisions.”
Michael Pourdehnad, MD, Senior Vice President, Head of Early Clinical Development, Hematology, Oncology and Cell Therapy.
In addition to our work with these partners, we’ve begun to run some research programs ourselves. We’ve acquired Mithril Medicine, a one-person company that has been developing treatments for two rare forms of muscular dystrophy. Its founder, Cody Tranbarger, used Claude to organize contracts, logistics, and experiments with 11 external labs, and delegated many other aspects of Mithril’s operations to Claude. As a result, Mithril took a possible treatment for one form of muscular dystrophy from an idea to promising results in mice in less than 18 months. Now at Anthropic, Cody is continuing this research, and plans to start several new programs. If his drug candidates continue to hold up, we expect a drug to be ready to start clinical trials in humans as soon as late 2028. Read more and see our technical report.
Extending our work in the life sciences
All of this research is still at an early stage. In particular, so far it is mostly preclinical—that is, for the most part, it isn’t yet at the stage where the drugs are being tested in humans. We plan to continue extending Claude’s role into clinical trials as these drug programs advance, and as we partner with more organizations whose drug development programs are already at the clinical stage.
This research is only one part of our efforts to accelerate drug discovery. Ultimately, we want to share our models with as many drug development researchers as possible. To that end, we’ve rolled out Claude Science, our AI workbench for researchers, and we’re providing Claude credits for life scientists. We’ve also created the Life Sciences Verification Program to allow trusted organizations to use our most powerful models with more permissive versions of our safeguards, which are necessary to prevent misuse but which can impede legitimate biological research. We’ve also recently launched an open call for guest investigators in the life sciences; we’re seeking proposals from research groups who want to use Claude to investigate key questions in drug discovery and other areas.
We’re looking forward to working with many more organizations as we expand our life sciences research. If you work at an organization focused on drug discovery and are interested in collaborating with us, you can get in touch below.
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