Sundar Pichai announces ‘exciting milestone’ as Google AI pairs up with Yale to discover new cancer therapy pathway

Alphabet CEO Sundar Pichai on Wednesday shared an “exciting milestone” achieved by DeepMind’s Gemma. The AI model, in a research collaboration with Yale University, developed a new pathway for potential cancer therapy.

In a post on X, the Google CEO announced that the tech giant had developed a new cancer-therapy hypothesis.

“An exciting milestone for AI in science: Our C2S-Scale 27B foundation model, built with @Yale and based on Gemma, generated a novel hypothesis about cancer cellular behavior, which scientists experimentally validated in living cells,” he wrote.

“With more preclinical and clinical tests, this discovery may reveal a promising new pathway for developing therapies to fight cancer,” Pichai added.

What's Google AI's new cancer therapy milestone?

In a blog post, Google explained how the cancer therapy pathway may work in the future.

The technology, Cell2Sentence-Scale 27B (C2S-Scale), is a new 27 billion parametre foundation model designed to understand the language of individual cells.

The model has been built on the Gemma family of open models.

“C2S-Scale generated a novel hypothesis about cancer cellular behavior and we have since confirmed its prediction with experimental validation in living cells. This discovery reveals a promising new pathway for developing therapies to fight cancer,” Google wrote in its blog.

Researchers gave the new model a task — to find a drug that acts as a conditional amplifier and would boost the body's immune signal only in a specific “immune-context-positive” environment.

“This required a level of conditional reasoning that appeared to be an emergent capability of scale; our smaller models could not resolve this context-dependent effect,” it said.

Researchers then simulated the effect of over 4,000 drugs across different contexts and asked the model to predict which drugs would only boost antigen presentation in the positive immune context.

“Out of the many drug candidates highlighted by the model, a fraction (10-30%) of drug hits are already known in prior literature, while the remaining drugs are surprising hits with no prior known link to the screen,” as per the blog.

The model identified a striking “context split” for the kinase CK2 inhibitor called silmitasertib (CX-4945). The model predicted a strong increase in antigen presentation when silmitasertib was applied in the “immune-context-positive” setting.