Anthropic Wants to Develop Its Own Drugs

New Delhi, India Jul 3, 2026 ALN: 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 bio and pharma customers already using Claude.

Anthropic also went a step further, saying it would develop drugs of its own. Head of life sciences Eric Kauderer-Abrams stated that the company will focus on discovering treatments for “neglected” diseases.

AI companies have been eager to court science and pharma customers — OpenAI, Amazon, Google, and others have their own life sciences tools and platforms. However, Anthropic’s planned move is one of the most direct public attempts by a major frontier AI company to actually develop drugs itself. This positions the company uniquely, as it will be 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, bio startups, and Big Pharma companies building or acquiring AI tools of their own.

Anthropic has provided very few specific details about what it hopes to accomplish in the drug development space. At the event, Kauderer-Abrams did not specify what the company would do if it finds any promising drug candidates. Anthropic did not respond to requests for comment seeking more details, including what diseases it plans to target first and whether it would partner with other companies for lab work, animal testing, clinical trials, or manufacturing.

AI is applied at “every single stage of drug discovery.”

Experts have noted 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 bio 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 added. Matthew Todd, a professor of drug discovery at University College London, echoed this sentiment, stating that AI already pervades drug discovery and research, calling it a “catchall phrase” given its broad array of uses.

AI is undoubtedly changing drug development. Han pointed to numerous initiatives by pharma giants like AstraZeneca, Novo Nordisk, and GSK, noting that AI can help generate possible drug ideas, such as by suggesting new molecules that could interact with parts of the body like cell receptors involved with particular diseases or targets of existing drugs. Todd remarked that AI is 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.

However, this is still a long way from an AI-designed drug reaching patients. Todd emphasized that 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, requiring human input and supervision throughout. Both Todd and Han noted that the lack of publicly available, high-quality experimental data, such as how various chemicals behave in the body, could slow drug development efforts, stressing that even for well-studied areas of biology, there are still significant gaps in understanding how things work.

AI models “haven’t yet come close to making experiments unnecessary.”

AI is not positioned to fix many of the slowest parts of drug discovery. 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, stated that while excitement about advancing AI models is warranted, they “haven’t yet come close to making experiments unnecessary.” Drug candidates still need to be tested in the real world for efficacy, toxicity, and practical properties that allow them to be prepared, stored, and delivered safely as medicines. All of this requires skilled workers, substantial funding, and time, especially for clinical work in humans — a stage where many promising drug candidates fail. If Anthropic wants to develop a drug, von Delft remarked, it will need to invest heavily in experiments.

It seems 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 now, it has several live applications seeking candidates for life sciences roles. Han mentioned that Anthropic has been “actively recruiting” in this area, adding that several of his academic colleagues have been approached by the company. Without naming names, Han believes Anthropic has successfully hired a few candidates away from Big Pharma and prestigious academic institutions.

Given the complexity of drug development, whatever disease Anthropic chooses to target, any potential payoff is likely a long way off — at least a decade, considering the typical duration for a new drug to undergo clinical trials. There is “always a big lag time” with testing medicine, Todd noted, stating, “It takes time to show experimentally that something’s safe.” No AI-designed drug has yet made it through clinical trials and FDA approval to reach the market. Some AI-developed candidates have entered clinical trials, but it remains unclear how much AI contributed, where in the process it was utilized, or whether those candidates outperform conventional drugs. While AI can expedite parts of the search, drugs still need to prove themselves through traditional, methodical experiments conducted in the real world.