Yilong Zhou, a visiting undergraduate student from Tsinghua University, used CRISPR-GPT to successfully active genes in A375 melanoma cancer cells as part of his research into better understanding why cancer immunotherapy sometimes fails.

Zhou typed his question into CRISPR-GPT’s text box: “I plan to do a CRISPR activate in a culture of human lung cells, what method should I use?”

CRISPR-GPT responded like an experienced lab mate advising a new researcher. It drafted an experimental design and, at each step, explained its “thought” process, describing why the various steps were important.

“I could simply ask questions when I didn’t understand something, and it would explain or adjust the design to help me understand,” Zhou said. “Using CRISPR-GPT felt less like a tool and more like an ever-available lab partner.”

As an early-career scientist, Zhou had designed only a handful of CRISPR experiments prior to using CRISPR-GPT. In this experiment, it took him one attempt to get it right — a rarity for most scientists.

In the past, Zhou was constantly worrying about making mistakes and double-checking his designs.

Reducing error and increasing accessibility

CRISPR-GPT can toggle between three modes: beginner, expert and Q&A. The beginner mode functions as a tool and a teacher, providing an answer and explanation for each recommendation. Expert mode is more of an equal partner, working with advanced scientists to tackle complex problems without providing additional context. Any researcher can use the Q&A function to directly address specific questions.

It’s also useful for sharing knowledge and collaborating with other labs, Cong said. CRISPR-GPT provides a more detailed and holistic response than what’s generally gleaned from a scientific manuscript and responds to repetitive inquires in a snap.

CRISPR-GPT can also check researchers’ work and apply experimental frameworks to new diseases the researchers may not be thinking about.

“People in my lab have been finding this tool very helpful,” Cong said. “The decisions are ultimately made by human scientists, but it just makes that whole process — from experiment design to execution — super simple.”

Editing responsibly and future expansion

While the technology is promising for accelerating therapeutic research, there are still some safety concerns to address before pushing CRISPR-GPT more broadly.

Cong and his team have already incorporated safeguards to protect the AI tool from irresponsible uses. For instance, if the AI receives a request to assist with an unethical activity, such as editing a virus or human embryo, CRISPR-GPT will issue a warning to the user and respond with an error message, effectively halting the interaction. Cong also plans to bring the technology to government agencies, such as the National Institute of Standards and Technology, to ensure ethical use and sound biosecurity.

In the future, the tool may serve as a blueprint for training AI to execute specific biological tasks outside of gene editing. From developing new lines of stem cells as experimental models, to deciphering molecular pathways involved in heart diseases, Cong hopes to expand the technology to other disciplines building a range of AI agents to aid in genomic discovery. To that end, he and his team developed the Agent4Genomics website, where they host a range of related AI tools for scientists to use and explore.

Researchers at Google DeepMind, Princeton University and the University of California, Berkeley contributed to this study.

Funding for this research came from the National Institute of Health (grants 1R35HG011316 and 1R01GM1416), the Donald and Delia Baxter Foundation Faculty Scholar Award, the Weintz Family Foundation, and the National Science Foundation.

What You Should Know:

– Causaly today announced Causaly Agentic Research, an agentic AI platform designed specifically for life sciences research and development.

– Built to deliver transparency and scientific rigor, the platform’s specialized AI agents access, analyze, and synthesize internal and external biomedical knowledge and competitive intelligence—so teams can automate complex workflows, uncover novel insights, and move from hypothesis to decision faster and with greater confidence. 

A scientific AI built for how researchers actually work

Extending Causaly Deep Research, the new offering introduces a conversational interface that lets scientists partner directly with AI research agents. Unlike general-purpose tools or static literature review software, Causaly’s agents are trained for life sciences R&D and securely unify internal and external data into a single source of truth. They execute multi-step research tasks—from hypothesis generation and testing to structured, transparent outputs—always grounded in traceable evidence. 

“Agentic AI fundamentally changes how life sciences conducts research,” said Yiannis Kiachopoulos, co-founder and CEO of Causaly. “Causaly Agentic Research emulates the scientific process—analyzing data, mapping biological relationships, and reasoning through problems—so scientists can reduce manual work, de-risk decisions, and focus on higher-value science.” 

Solving the bottlenecks slowing discovery

R&D organizations grapple with vast, rapidly expanding biomedical information. Manual, siloed processes lengthen cycles, hide critical signals, and introduce bias. By combining extensive biomedical sources with competitive intelligence and proprietary datasets in one intelligent interface that fits existing workflows, Causaly helps teams break down silos, boost productivity, and accelerate ideas to market. 

What the agents do

• Advance complex analyses and provide answers that propel research forward
• Verify quality and accuracy, compressing time-to-discovery
• Continuously scan the landscape to surface critical signals and emerging evidence in real time
• Deliver fully traceable insights to support confident, evidence-backed decisions and regulatory rigor
• Connect across systems and data—including public apps and other AI agents—to unify discovery workflows