Apply for Anthropic’s AI for Science rare disease research grants
Apply for Anthropic’s AI for Science rare disease research grants
申请 Anthropic 的“AI for Science”罕见病研究资助
Last spring, we announced Anthropic’s AI for Science program, an initiative designed to accelerate scientific research and discovery through access to our API. Since launching, we have supported researchers working on a variety of high-impact projects, ranging from drug repurposing to quantum simulation. 去年春天，我们宣布了 Anthropic 的“AI for Science”计划，旨在通过提供我们的 API 访问权限来加速科学研究与发现。自启动以来，我们已经支持了多项具有重大影响力的研究项目，涵盖了从药物重定向到量子模拟等多个领域。
Throughout this initiative, we have found that projects are more generative when multiple AI for Science grantees are working on related questions and exchanging tips. So we now plan to launch thematic calls for projects within the broader AI for Science program. Today, we are sharing a focused call for applications centered specifically on rare genetic diseases. 在这一计划的推进过程中，我们发现当多位“AI for Science”受资助者共同研究相关课题并交流经验时，项目往往更具创造力。因此，我们计划在更广泛的“AI for Science”框架内发起主题项目征集。今天，我们发布了一项专门针对罕见遗传病的重点申请征集。
Accepted applicants will receive up to $50,000 in Claude credits over six months, with the goal of building a community of researchers looking into how AI can reshape our understanding of rare disease. This program has two tracks: one for scientists doing basic research, and another for early-stage biotechs working on speeding up clinical development for rare diseases. 入选者将在六个月内获得最高 5 万美元的 Claude 额度，旨在建立一个研究者社区，共同探讨 AI 如何重塑我们对罕见病的认知。该计划分为两个方向：一个是面向从事基础研究的科学家，另一个是面向致力于加速罕见病临床开发的早期生物技术公司。
Rare disease research is an area where knowledge of fundamental science is limited. In aggregate, rare diseases are among the most prevalent conditions on the planet (an estimated 400 million people live with one of more than 7,000 rare diseases). But these conditions are scattered across small populations, making it challenging for clinicians to build patient registries, identify promising therapeutic targets, and design clinical trials. 罕见病研究是一个基础科学知识相对匮乏的领域。总体而言，罕见病是全球最普遍的疾病之一（据估计，有 4 亿人患有 7,000 多种罕见病中的一种）。但这些疾病分散在不同的小群体中，使得临床医生在建立患者登记册、识别有前景的治疗靶点以及设计临床试验方面面临巨大挑战。
Moreover, rare diseases are typically characterized by their unique features (such as a specific genetic variation or combination of symptoms) that are often studied in isolation, making it nearly impossible to spot mechanisms shared across diseases. Finally, rare diseases face a challenge endemic to all drug development: the time it takes to move promising drug candidates into patient trials. 此外，罕见病通常具有独特的特征（如特定的基因变异或症状组合），且往往被孤立研究，这使得发现不同疾病间的共同机制几乎成为不可能。最后，罕见病还面临着所有药物开发中固有的挑战：将有前景的候选药物推进到患者临床试验所需的时间过长。
We think AI can help with these and related challenges. AI makes it possible to accurately model rare genetic diseases and detect patterns across them. It also helps researchers synthesize findings across a large corpus of literature, quickly extract information from limited datasets, and create shared terminology, all of which informs how researchers can better use the information they do have, even as more work is done to generate more data and address challenges pertaining to access and geography. 我们认为 AI 可以帮助解决这些及相关挑战。AI 能够精确建模罕见遗传病并检测其间的模式。它还能帮助研究人员综合大量文献的研究结果，从有限的数据集中快速提取信息，并创建共享术语。即便在生成更多数据以及解决获取和地理限制挑战的同时，这些能力也能指导研究人员更好地利用现有信息。
To explore where AI can be most helpful, we’ve made rare diseases the focus of our current call for AI for Science projects. 为了探索 AI 在哪些方面最能发挥作用，我们将罕见病定为当前“AI for Science”项目征集的核心重点。
Track one: Scaling our basic science partnerships
方向一：扩大基础科学合作伙伴关系
The first track of our rare disease research grants program aims to foster collaboration between clinical researchers, patient organizations, and data scientists to increase the pace of progress in basic science and the discovery of the mechanisms underlying rare diseases. 我们罕见病研究资助计划的第一个方向旨在促进临床研究人员、患者组织和数据科学家之间的合作，以加快基础科学的进展，并发现罕见病背后的潜在机制。
An early partner in this effort is the Monarch Initiative, an international consortium working to improve diagnosis and mechanism discovery for patients with rare diseases. Monarch develops standards and resources such as the Mondo Disease Ontology, a computational framework and coding system that reconciles disease definitions scattered across OMIM, Orphanet, ICD, and dozens of other sources; as well as the Monarch Knowledge Graph, which integrates genotype-phenotype data across species to aid diagnostics and mechanism discovery. 该领域的早期合作伙伴是 Monarch Initiative，这是一个致力于改善罕见病患者诊断和机制发现的国际联盟。Monarch 开发了诸如 Mondo 疾病本体论等标准和资源，这是一个计算框架和编码系统，旨在协调分散在 OMIM、Orphanet、ICD 及其他数十个来源中的疾病定义；此外还有 Monarch 知识图谱，它整合了跨物种的基因型-表型数据，以辅助诊断和机制发现。
Most recently, Monarch contributors have been stitching data and knowledge together in a new agent-friendly mechanistic disease classification library called DisMech, where Claude can read case reports, variant databases, registry schemas, raw public data, and more, and point out mechanistic similarities between diseases at an unmatched pace and scale. Monarch is inviting our AI for Science grantees to use and contribute to its resources, such as Mondo and DisMech, to reveal new mechanistic hypotheses that will support developing treatments. 最近，Monarch 的贡献者们正在将数据和知识整合到一个名为 DisMech 的新型智能体友好型机制疾病分类库中。Claude 可以读取其中的病例报告、变异数据库、登记模式、原始公共数据等，并以无与伦比的速度和规模指出疾病之间的机制相似性。Monarch 邀请我们的“AI for Science”受资助者使用并为其资源（如 Mondo 和 DisMech）做出贡献，以揭示支持药物开发的新机制假设。
Monarch’s work on improving the interoperability of rare disease data and knowledge is a place where Claude can already have a major impact. However, there’s more work to be done to gather better and more data, improve diagnostic infrastructure, and promote patient-led approaches across the rare disease ecosystem, and make the information accessible to agentic science. We will continue to partner with Monarch and others to approach this problem from the angles where AI is less obviously applicable, and we’ll share what we learn as we do. Monarch 在提高罕见病数据和知识互操作性方面的工作，正是 Claude 已经能够产生重大影响的领域。然而，在收集更好、更多的数据，改善诊断基础设施，在整个罕见病生态系统中推广以患者为主导的方法，以及使信息能够被智能体科学所利用等方面，还有更多工作要做。我们将继续与 Monarch 及其他机构合作，从 AI 应用尚不明显的角度切入这一问题，并分享我们的研究心得。
Track two: Scaling our biotech partnerships
方向二：扩大生物技术合作伙伴关系
The second track of our rare disease research grants program will support biotechnologists and early-stage biotechs working to accelerate drug development for rare diseases. Today, it takes one to two years to move from a confirmed genetic diagnosis to a treatment available to patients, with much of this time spent waiting in queues for certified manufacturing slots, running safety studies sequentially instead of in parallel, and hand-assembling the thousands of pages of chemistry and regulatory documentation required for in-patient testing. 我们罕见病研究资助计划的第二个方向将支持致力于加速罕见病药物开发的生物技术专家和早期生物技术公司。目前，从确诊遗传病到患者获得治疗通常需要一到两年时间，其中大部分时间耗费在排队等待认证的生产名额、按顺序而非并行进行安全性研究，以及手工整理用于临床试验所需的数千页化学和监管文档上。
We think it is possible to radically compress phases of this process with Claude—in particular, by making it easier to complete documentation (for example, drafting and reviewing the regulatory dossier), but also by speeding up therapeutic strategy selection (for example, by analyzing whether a target is druggable across a suite of modalities, such as small molecules, antibodies, genetic medicines, and so on) and looking for shared mechanisms across individual genetic therapies, which could allow them to be approved under a single “basket trial” instead of requiring a separate IND for each patient. 我们认为，利用 Claude 有可能从根本上压缩这一过程的各个阶段——特别是通过简化文档编制（例如起草和审查监管档案），以及加速治疗策略的选择（例如分析靶点是否适用于小分子、抗体、基因药物等多种模式），并寻找不同基因疗法之间的共同机制，从而使它们能够通过单一的“篮子试验”获得批准，而无需为每位患者单独申请新药临床试验（IND）。
Although many aspects of drug development are difficult to expedite because of manufacturing constraints or safety testing, we believe much can be done to move more quickly. By granting API credits and Claude Science access to the many biotechnologist… 尽管药物开发的许多方面因制造限制或安全性测试而难以加速，但我们相信仍有许多工作可以做得更快。通过向广大生物技术专家提供 API 额度和 Claude Science 访问权限……