SandboxAQ brings its drug discovery models to Claude — no PhD in computing required

SandboxAQ 将其药物发现模型引入 Claude——无需计算机博士学位

Drug discovery is one of the most expensive failures in modern industry. Finding a single viable molecule can take a decade and cost billions, and most candidates still don’t make it. A generation of AI startups has promised to fix that — most have made the problem less painful for researchers, who are already technically sophisticated enough to use the tools. But SandboxAQ thinks the bottleneck isn’t the models. It’s the interface. The company has teamed up with Anthropic to integrate its scientific AI models directly into Claude — putting powerful drug discovery and materials science tools behind a conversational interface that requires no specialized computing infrastructure to use.

药物发现是现代工业中最昂贵的失败领域之一。寻找一个可行的分子可能需要十年时间，耗资数十亿美元，而且大多数候选分子最终都无法成功。一代人工智能初创公司曾承诺解决这一问题——大多数公司只是减轻了研究人员的负担，而这些研究人员本身已经具备了使用这些工具所需的技术水平。但 SandboxAQ 认为，瓶颈不在于模型，而在于交互界面。该公司已与 Anthropic 合作，将其科学 AI 模型直接集成到 Claude 中，通过对话式界面提供强大的药物发现和材料科学工具，用户无需任何专门的计算基础设施即可使用。

Founded roughly five years ago as an Alphabet spinout, SandboxAQ counts Eric Schmidt, Google’s former CEO, as its chairman. The company, which has raised more than $950 million from investors, has built out a number of different business lines, including a cybersecurity business. One of the more unique things SandboxAQ does, however, is produce large quantitative models, or LQMs. These proprietary models are “physics-grounded,” meaning they’re built on the rules of the physical world rather than patterns in text. They can run quantum chemistry calculations and simulate both molecular dynamics and microkinetics, the study of how chemical reactions unfold at the molecular level. That matters because it tells researchers how candidate molecules are likely to behave before anyone sets foot in a lab.

SandboxAQ 大约五年前从 Alphabet 分拆出来，其董事长是谷歌前首席执行官埃里克·施密特（Eric Schmidt）。该公司已从投资者处筹集了超过 9.5 亿美元，并建立了多个不同的业务线，包括网络安全业务。然而，SandboxAQ 最独特的地方之一是生产大型定量模型（LQMs）。这些专有模型是“基于物理的”，这意味着它们建立在物理世界的规则之上，而不是基于文本模式。它们可以运行量子化学计算，并模拟分子动力学和微观动力学（研究化学反应如何在分子水平上展开）。这一点至关重要，因为它能在研究人员进入实验室之前，就告诉他们候选分子的表现可能如何。

“Trained on real-world lab data and scientific equations, LQMs are AI models engineered for the quantitative economy, a $50+ trillion sector spanning biopharma, financial services, energy, and advanced materials,” the company said in a news release that strongly suggests Sandbox AQ isn’t building another chatbot or code assistant — it’s chasing the economy that AI is supposed to transform. Chai Discovery and Isomorphic Labs — both well-funded bets on better models — have focused on the science. SandboxAQ is focused on who can actually use it.

“LQMs 基于真实实验室数据和科学方程进行训练，是专为定量经济打造的 AI 模型。这是一个涵盖生物制药、金融服务、能源和先进材料的 50 多万亿美元规模的行业。”该公司在新闻稿中表示。这强烈暗示 SandboxAQ 并非在构建另一个聊天机器人或代码助手，而是在追逐 AI 本应变革的经济领域。Chai Discovery 和 Isomorphic Labs——这两家在更好模型上投入巨资的公司——都专注于科学本身，而 SandboxAQ 则专注于谁能真正使用这些技术。

“For the first time, we have a frontier [quantitative] model on a frontier LLM that someone can access in natural language,” Nadia Harhen, SandboxAQ’s general manager of AI simulation, told TechCrunch. Previously, users of SandboxAQ’s LQMs would have had to provide their own digital infrastructure to run the models. SandboxAQ’s customers tend to be computational scientists, research scientists, or experimentalists. Generally, these people work at large pharmaceutical or industrial companies and are searching for new materials that can become marketable products.

“我们首次在尖端大语言模型（LLM）上实现了尖端定量模型，用户可以通过自然语言进行访问，”SandboxAQ AI 模拟总经理 Nadia Harhen 告诉 TechCrunch。此前，SandboxAQ 的 LQM 用户必须提供自己的数字基础设施来运行这些模型。SandboxAQ 的客户通常是计算科学家、研究科学家或实验人员。通常，这些人就职于大型制药或工业公司，正在寻找可以转化为市场化产品的新材料。

“Our customers come to us because they’ve tried all the other software out there, and the complexity of their problem is such that it didn’t work or didn’t yield positive results for them when that translation went to take place in the real world,” said Harhen.

“我们的客户找到我们，是因为他们已经尝试了市面上所有的其他软件，但他们面临的问题极其复杂，以至于在现实世界中进行转化时，那些软件要么无法运行，要么无法产生积极的结果，”Harhen 说道。