$60 Billion Couldn't Fix AI Drug Discovery. Can Anthropic?
I’ve been following AI’s entry into biomedicine for a long time. Most of my DNA and health data analysis I try to run with Claude Code. So when Anthropic makes a move in this space, I pay closer attention than usual.
Claude Science
On June 30, Anthropic released Claude Science, an AI workbench for scientists, combining databases, coding tools, compute, and research workflows in a single environment.
The problem it’s solving is real and specific. Anyone who has done serious computational research knows the daily grind: dozens of databases, each with its own schema and query language. You’re not doing science, you’re doing logistics.
Claude Science aims to remove that friction. The platform comes preconfigured with access to more than 60 scientific databases and includes specialized tools for genomics, proteomics, structural biology, single-cell research, and chemistry.
The architecture is built around full provenance. Every output includes the exact code, execution environment, plain-language description, and message history that produced it, enabling reproducibility months later. That last part matters more than it sounds. Reproducibility is science’s chronic illness, and Anthropic is aiming directly at it.
A coordinating agent hands tasks to specialist sub-agents, while a separate reviewer agent checks every citation and calculation and fixes errors as it goes. The reviewer is doing something that should have existed years ago: internal peer review before the paper leaves the lab.
Data stays on the researcher’s own infrastructure. Large and sensitive datasets go nowhere, only the context needed for each analytical step is sent to Claude.
One early case study: Manifold Bio uses Claude Science to nominate targets for experiments evaluating surface expression, trafficking, and safety for each tissue and target, then ranking candidates according to criteria learned from their own internal data. They say ordinary coding assistants can’t do this end-to-end.
Claude Science is not a new AI model. It runs on the same Claude models available to paying subscribers, including Opus 4.8. The bet here is entirely on workflow, not raw model capability. That’s a more honest position than most AI product launches take.
The bigger move
But the product wasn’t the main story.
Alongside Claude Science, life sciences leaders Eric Kauderer-Abrams and Jonah Cool announced that Anthropic would run its own internal drug discovery program, targeting neglected diseases that traditional biopharma companies wouldn’t pursue.
This is the unusual part. The standard playbook is: AI company builds tools, pharma companies do the drug hunting. Anthropic is collapsing that division.
Kauderer-Abrams said the reason is simple: “We need to live it along with all of you. We believe in the power of tight feedback loops, and there’s no substitute for having our own experiences alongside you all in the trenches trying to develop drugs.”
There’s a logic to it. If you’re selling workflow tools to biopharma, having your own programs gives you credibility and a real feedback loop. You can’t build the right instrument if you’ve never played the music.
As a public benefit company, Anthropic says they “can choose programs on patient benefit, including work the commercial market overlooks.”
That framing - public benefit, neglected diseases, the patients the healthcare system discards first is deliberate. The healthcare system is optimized for scale and standardization. Rare diseases get cut early. Not enough patients, no profitable target, but the burden for those living with it is completely real.
The infrastructure behind the bet
In April, Anthropic acquired Coefficient Bio, a stealth New York-based startup, in an all-stock deal valued at just over $400 million. The team is fewer than 10 people, most of them former Genentech computational biology researchers, and joins Anthropic’s Healthcare and Life Sciences division.
Coefficient Bio’s co-founders, Samuel Stanton and Nathan C. Frey, both came from Prescient Design, Genentech’s computational drug discovery unit, where they worked on experimental design for scientific discovery and contributed to projects including Cortex, a modular deep learning architecture for drug discovery.
Anthropic has also opened wet labs to run its own basic research. This is not a software company making announcements about biology. They’re building actual laboratory infrastructure.
Kauderer-Abrams was hired with an explicit mandate: make Claude the dominant AI model in biology. “We want a meaningful percentage of all of the life science work in the world to run on Claude, in the same way that that happens today with coding.”
The financial context: Anthropic is currently valued at $965 billion, more than any health company except Eli Lilly, and has filed confidential IPO paperwork.
The honest assessment
I’m watching all this with genuine interest, but I’m staying clear-eyed.
OpenAI launched GPT-Rosalind in April 2026 - a model specifically fine-tuned for biological reasoning. OpenAI’s own LifeSciBench, built with 173 PhD scientists, found that even the best-performing model cleared only 36.1% of real research tasks. OpenAI’s own life sciences lead acknowledged that AI cannot yet create new disease treatments on its own.
The broader picture is harder. Since 2019, roughly $60 billion has poured into AI drug development. Around 175 programs have reached clinical trials. Not a single FDA-approved drug has come out of it yet.
The harder problems- training models on ambiguous biological ground truth, building causal understanding of disease, identifying the right patients for the right drugs are still in early innings.
What Anthropic has that most of the earlier wave didn’t: a foundation model that genuinely reasons, wet labs creating real feedback loops, a team with Genentech-level computational biology credentials, and the financial firepower to stay in the game long enough for it to matter.
The bet is that combining foundation model reasoning with wet-lab feedback loops and biotech operational expertise can compress timelines across the board. Whether the ten-fold acceleration materializes will depend on how fast the training data problems get solved, and whether the models can learn to go beyond human knowledge by closing the loop with experimental results.
That’s the question worth watching. Not the product launch. The question of whether Anthropic can change the picture in a space where the rest of the industry has so far fallen short.
I’m curious what you think. Is this move different or are we watching another well-funded wave that will break the same way the last $60 billion did? Drop your take in the comments.