Anthropic Thinks It’s The Right Time To Start Developing Drugs

Anthropic plans to develop its own drugs targeting neglected diseases, marking one of the clearest moves yet by a major artificial intelligence company into pharmaceutical development, even as experts say AI-designed medicines remain years from reaching patients.

The AI startup unveiled Claude Science, which it described as an AI workbench that brings together scientific tools and datasets in a single environment and can generate figures and visualizations for researchers.

The company said the platform is designed to help accelerate scientific research and highlighted a growing roster of biotechnology and pharmaceutical customers using its AI models.

At the launch event, Anthropic’s head of life sciences, Eric Kauderer-Abrams, said the company would also pursue drug discovery, focusing on treatments for “neglected” diseases.

The move would put Anthropic in direct competition with some of the pharmaceutical companies that also use its software, while placing it alongside AI-focused drug developers such as Isomorphic Labs, Insilico Medicine and a growing number of biotechnology startups and large drugmakers investing in AI.

Anthropic has disclosed few details about its plans. Kauderer-Abrams did not say how the company would advance any promising drug candidates or whether it would partner with other companies for laboratory research, animal testing, clinical trials or manufacturing. Anthropic did not respond to requests for comment from The Verge seeking further details.

Industry experts said AI is already widely used throughout pharmaceutical research but cautioned against expectations that it will rapidly produce new medicines.

“AI drug discovery” “is a really broad term,” said Namshik Han, a professor at the University of Cambridge and co-founder of AI biotechnology startup CardiaTec.

AI is applied at “every single stage of drug discovery,” he said, from identifying potential compounds and optimizing them to analyzing research data, supporting clinical trials and improving manufacturing.

Matthew Todd, a professor of drug discovery at University College London, said AI has become a “catchall phrase” because of its broad use across pharmaceutical research.

Researchers say generative AI can help scientists search vast chemical and biological datasets, identify promising molecules and uncover new disease targets or uses for existing drugs.

However, Todd said the field is “a long way off” from producing an AI-designed drug approved for human use by regulators. Human oversight remains essential throughout the drug development process, he added.

Han and Todd also said limited availability of high-quality experimental data continues to constrain AI-driven drug research, even in well-studied areas of biology.

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

Drug candidates must still undergo laboratory testing, animal studies and human clinical trials to demonstrate safety and effectiveness, he said.

“If Anthropic wants to develop a drug,” von Delft said, it is “going to have to spend a lot on experiments.”

Anthropic has expanded its life sciences efforts over the past year, hiring biologists, building laboratory facilities and recruiting researchers from academia and the pharmaceutical industry, according to Han.

Even if successful, experts said any commercial payoff is likely years away. Developing a new medicine typically takes more than a decade from discovery through clinical testing and regulatory approval.

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 completed clinical trials and received approval from the U.S. Food and Drug Administration for sale. While some AI-assisted drug candidates have entered clinical trials, researchers say it remains difficult to determine how much AI contributed to their development or whether they outperform conventionally developed medicines.

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