Researchers at Google DeepMind have unveiled their latest artificial intelligence tool and claimed it will help scientists identify the genetic drivers of disease and ultimately pave the way for new treatments.

AlphaGenome predicts how mutations interfere with the way genes are controlled, changing when they are switched on, in which cells of the body, and whether their biological volume controls are set to high or low.

Most common diseases that run in families, including heart disease and autoimmune disorders, as well as mental health problems, have been linked to mutations that affect gene regulation, as have many cancers, but identifying which genetic glitches are to blame is far from straightforward.

“We see AlphaGenome as a tool for understanding what the functional elements in the genome do, which we hope will accelerate our fundamental understanding of the code of life,” Natasha Latysheva, a DeepMind researcher, told a press briefing on the work.

The human genome runs to 3bn pairs of letters – the Gs, Ts, Cs and As that comprise the DNA code. About 2% of the genome tells cells how to make proteins, the building blocks of life. The rest orchestrates gene activity, carrying the crucial instructions that dictate where, when and how much individual genes are switched on.

The researchers trained AlphaGenome on public databases of human and mouse genetics, enabling it to learn connections between mutations in specific tissues and their impact on gene regulation. The AI can analyse up to 1m letters of DNA code at once and predict how mutations will affect different biological processes.

The DeepMind team believes the tool will help scientists map out which strands of genetic code are most essential for the development of particular tissues, such as nerve and liver cells, and pinpoint the most important mutations for driving cancer and other diseases. It could also underpin new gene therapies by allowing researchers to design entirely new DNA sequences – for example, to switch on a certain gene in nerve cells but not in muscle cells.

Carl de Boer, a researcher at the University of British Columbia in Canada, who was not involved in the work, said: “AlphaGenome can identify whether mutations affect genome regulation, which genes are impacted and how, and in what cell types. A drug could then be developed to counteract this effect.

“Ultimately, our goal is to have models that are so good we don’t have to do an experiment to confirm their predictions. While AlphaGenome represents a significant innovation, achieving this goal will require continued work from the scientific community.”

Some scientists have already begun using AlphaGenome. Marc Mansour, a clinical professor of paediatric haemato-oncology at UCL, said it marked a “step change” in his work to find genetic drivers for cancer.

Gareth Hawkes, a statistical geneticist at the University of Exeter, said: “The non-coding genome is 98% of our 3bn base pair genome. We understand the 2% fairly well, but the fact that we’ve got AlphaGenome that can make predictions of what this other 2.94bn base pair region is doing is a big step forward for us.”