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.
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.
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.
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.
AI is applied at “every single stage of drug discovery.”
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.
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.
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.
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, 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.”
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. Han said Anthropic has been “actively recruiting” in the area too, adding that several of his academic colleagues had been approached by the company. Without naming names, Han said he thinks Anthropic has successfully hired a few candidates away from Big Pharma and prestigious academic institutions.
With all of this complexity, whatever disease Anthropic picks, any payoff is likely a long way away — at the very least, the better part of a decade, given how long it typically takes a new drug to go through clinical trials. There’s “always a big lag time” with testing medicine, Todd said. “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 market. Some AI-developed candidates have entered clinical trials, but it’s hard to know how much AI contributed, where in the process it was used, or whether those candidates outperform conventional drugs. AI can speed up part of the search, but drugs still need to prove themselves the old-fashioned way: in slow, methodical experiments that take place in the real world.
