Anthropic recently launched Claude for Life Sciences, which has the potential to be a breakthrough in AI-driven research, acting as a full-fledged assistant capable of accelerating discovery across the entire scientific lifecycle.
While multiple large language models (LLMs) exist, research shows that no single model excels at everything, GPT-4 may lead in medical reasoning, whereas a smaller, fine-tuned model like BioBERT can outperform others when extracting gene names from thousands of documents due to its speed and precision.
Talking to AIM, Purav Gandhi, founder & CEO, Healthark mentioned that compared to GPT-4 or Med-PaLM 2, Claude offers a distinct advantage. Healthark is a healthcare consulting firm that provides services in strategy, market access, and digital health.
“Those models take a fair bit of effort to configure and connect to real datasets. GPT-4, for example, isn’t natively tuned for genomics or clinical research,” said Gandhi, adding that Med-PaLM 2 works best in a narrow Q&A format and doesn’t easily connect with live R&D systems.
“That’s where Anthropic’s Claude for Life Sciences will hopefully be different.”
He mentioned that as it’s already tuned for scientific language and comes with built-in connectors for platforms like Benchling, Synapse, and PubMed, it should be far easier to plug into existing research workflows. And more importantly, its grounding in ethical, safety, and regulatory frameworks — something you can’t easily fine-tune into a model, will make it a better fit for real, compliant use in life sciences.
Commenting on the same, Soutick Saha, bioinformatics software developer, Wolfram told AIM, “I think it might be a very useful tool when used by experts. In particular, this might make computational analysis of large scale biological data easier for biologists with limited coding experience.”
In a recent podcast, Jonah Cool, head of Life Sciences at Anthropic, and Eric Kauderer-Abrams, head of biology and life sciences, discussed how they are developing Claude into a comprehensive tool that supports biologists at every stage, from early hypothesis generation to translational research.
Abrams mentioned that Claude starts to address some of the core problems in biology and it brings more fluidity, lowers the barrier for computational analysis, and helps make discoveries transferable across fields.
“Anthropic’s commitment to advancing life sciences goes right to the heart of our mission,” Abrams said. “The number one place that we are excited about applying AI is within biology and the life sciences. That’s where we believe it can have the most beneficial impact.”
Supporting the Full Research Lifecycle
The team is not limiting Claude to early-stage discovery problems like molecule design or protein folding.
“We want to address the whole spectrum from early stage discovery all the way through development and translation,” Abrams explained.
Claude is designed to interact with the full ecosystem of scientific tools, from Benchling for lab notebooks, 10x Genomics for single-cell analysis, to PubMed for literature review.
“From early stage hypothesis generation, when things are more creative, to drafting protocols, running experiments, computational analysis, and presenting results, Claude is starting to handle meaningful chunks of work that would take a human scientist hours,” Cool said in the podcast, adding, “It goes from being a useful utility to actually a brainstorming partner.”
This leap is powered by Sonnet 4.5, Anthropic’s latest model that is skilled in different domains of science and it is particularly strong at long horizon tasks consisting of long sequences of tool calls. That’s critical for complex bioinformatics workflows.
Integrating into Lab Workflows
Claude’s capabilities extend beyond lab experiments to literature reviews, protocol drafting, regulatory submissions, and problem-solving.
Looking ahead, Anthropic envisions a fully integrated workflow, from talking to Claude about an experiment, to designing an experimental plan, drafting protocols, running those experiments, and reviewing the data.
Gandhi said that Anthropic’s Claude for Life Sciences is shaping up to be quite interesting. “Unlike the usual large language models, this one has been trained with a lot more life sciences context — it actually understands the language, the dictionary, and the nuances of how science is written and spoken,” he said.
That makes a big difference in interpreting clinical data, bioinformatics outputs, or genomic information. The depth of understanding feels a lot more relevant to how scientists work, he added.
He further added that it is also unique
The way Claude can connect into different data environments through what Anthropic calls connectors is also unique, Gandhi said. Instead of copy-pasting data or manually switching between tools, it can pull from Benchling, Synapse, PubMed, or even cloud data platforms like Snowflake and Databricks, and also from local files sitting on the system, he said.
That ability to tap into sources of truth and derive insights quickly could change how R&D teams explore and make decisions, Gandhi added.
He said that Anthropic claims to have built this model with a strong focus on ethics, safety, and regulatory alignment — basically training it to ‘think’ within compliant boundaries. “That matters a lot in a regulated industry like ours.”
Early pilots with Novo Nordisk and 10x Genomics have shown encouraging results, which is a good sign. That said, like any other LLM, it can still miss things or hallucinate. The way to think about Claude, at least for now, is as a really smart assistant, not a black-box scientist. It can accelerate thinking, but it still needs human judgment on top.
Anthropic’s mission is to accelerate the pace of scientific progress by creating AI that augments human intelligence rather than replacing it.
“Our goal is to accelerate 100 years of science in 10. And with Claude, we’re finally at the moment where all of this is possible,” Cool added.
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