Anthropic is launching its own drug discovery programs for neglected diseases.

The company plans to research treatments for diseases that traditional pharma and biotech firms consider unprofitable, focusing on early, preclinical-stage drug development. Anthropic says the move aligns with its nonprofit mission and will help it build better AI models and tools for the broader industry through firsthand experience. The announcement (1:08:34) came during an event for the company's new science AI tool "Claude Science".

The event also featured early examples of how AI could speed up medical research. A researcher at UCSF used Claude Science to spot a viral contamination in minutes that his team had missed for an entire year, according to Anthropic. The company also says Claude analyzed 100 rare genetic diseases in under an hour and flagged 32 candidates for computational screening.

Small gains, massive impact

Novartis CEO Vas Narasimhan said that getting a finished drug candidate from development to approval currently takes about twelve years. He broke the delays into three categories: information latency, operational latency, and biological latency.

New tools and models could sharply cut the first two categories, which account for roughly 40 percent of total development time. Biological latency, the time needed for animal testing, cell models, and human clinical trials, won't shrink much. That could bring development timelines down to seven or eight years.

Narasimhan also sees room to double success rates from 8 to 16 percent. Better safety predictions and optimized molecular properties could help, though the effect of improved patient selection remains unclear. The biggest challenge is still figuring out whether a drug target is biologically the right one for a given disease.

According to Narasimhan, even these seemingly modest gains would be huge when scaled across major pharma. Together, the big companies spend $150 to $200 billion a year on R&D and have produced only 800 to 1,000 drugs in 120 years. More diseases could be treated, and drug targets that were previously considered unreachable could become viable.

AI across healthcare

Other AI companies are also pushing into medicine. Deepmind CEO Demis Hassabis co-founded Isomorphic Labs with Alphabet to apply AI directly to drug discovery. Google Deepmind's protein structure prediction tool AlphaFold remains one of the most prominent examples of AI in biology, and its co-developer John Jumper recently left for Anthropic.

On the clinical side, Google DeepMind introduced an AI Co-Clinician in 2026 built around triadic care. AI agents support patients throughout treatment while the physician retains clinical authority.

OpenAI has also been moving into healthcare over the past few years. In early 2026, it launched ChatGPT Health, a dedicated health section within ChatGPT that lets users connect medical records, Apple Health data, and wellness apps.

Independent experts still urge caution, especially when AI is used in clinical settings for diagnoses, treatment plans, and direct patient care. Catherine Pope of the University of Oxford called the results so far "a piece removed from the messy, complex, human world of everyday healthcare."