Google DeepMind’s AlphaGenome predicts genetic mutations without lab tests

Google’s DeepMind has unveiled AlphaGenome, an AI model that predicts how small DNA changes affect gene activity and molecular processes. The breakthrough technology represents a significant leap beyond the company’s Nobel Prize-winning AlphaFold protein-folding system, potentially accelerating genetic research and medical diagnostics by allowing certain lab experiments to be conducted virtually.

What you should know: AlphaGenome unifies multiple genomic analysis challenges into a single AI system that can predict genetic variant effects at the molecular level.

• The model analyzes how changing individual DNA letters affects gene activity, answering questions that typically require time-consuming laboratory experiments.
• “We have, for the first time, created a single model that unifies many different challenges that come with understanding the genome,” says Pushmeet Kohli, a vice president for research at DeepMind.
• Google plans to make AlphaGenome free for noncommercial users while exploring commercial licensing opportunities for biotech companies.

How it works: The system uses Google’s transformer architecture—the same technology powering large language models like ChatGPT—trained on extensive experimental data from public scientific projects.

• Researchers can input genetic variants to receive predictions about their molecular effects without conducting physical experiments.
• “You’ll get this list of gene variants, but then I want to understand which of those are actually doing something, and where can I intervene,” explains Caleb Lareau, a computational biologist at Memorial Sloan Kettering Cancer Center.

Why this matters: The tool addresses a fundamental challenge in genetics—understanding what the 3 billion letters of human DNA actually do and how individual differences affect health.

• Studies often reveal thousands of genetic differences that slightly alter disease risk, but determining which variants are actually functional requires extensive lab work.
• “This is the most powerful tool to date to model that,” says Lareau, who has had early access to AlphaGenome.

Medical applications: AlphaGenome could transform rare disease diagnosis and cancer treatment by identifying which genetic mutations are driving specific conditions.

• Doctors treating patients with ultra-rare cancers could use the system to pinpoint which mutations are causing the underlying problem, potentially guiding treatment decisions.
• “A hallmark of cancer is that specific mutations in DNA make the wrong genes express in the wrong context,” notes Julien Gagneur, a professor of computational medicine at the Technical University of Munich.
• The technology could help diagnose rare genetic diseases where patients never learn the source of their condition despite having their DNA sequenced.

Important limitations: Google emphasizes that AlphaGenome focuses on molecular-level gene activity rather than personal genomic predictions.

• The system won’t provide 23andMe-style revelations about individual traits or ancestry.
• “We haven’t designed or validated AlphaGenome for personal genome prediction, a known challenge for AI models,” Google stated.

The bigger vision: DeepMind positions AlphaGenome as a stepping stone toward more ambitious goals in computational biology.

• CEO Demis Hassabis has expressed aspirations to “simulate a virtual cell,” while some researchers envision using AI to design entire genomes and create new life forms.
• Kohli describes AlphaGenome as a “milestone” that’s “starting to sort of shed light on the broader semantics of DNA.”