AI just generated new scientific knowledge: Google used AI to make a new cancer hypothesis - and it worked

Google’s new C2S-Scale 27B model, built with Yale, identified a drug that boosts immune response in “cold” tumors - a prediction never seen in literature.

Scientists tested it in human cells.

It worked: a 50% increase in antigen presentation, the process that helps T-cells recognize and attack cancer 🤯

That’s not a search result. That’s a scientific discovery.

Here’s how it happened:

→ C2S-Scale treats cellular data like a language, trained on massive single-cell datasets.

→ It ran a virtual screen across 4,000+ compounds to find one that works only when immune signals are low.

→ The model pointed to silmitasertib, a known CK2 inhibitor, but never linked to immune amplification.

→ Human validation confirmed the effect.

This could be huge because:

↳ 70-80% of cancer patients don’t respond to immunotherapy because their tumors stay “cold.”

↳ This could help “heat them up” - making hidden cancers visible to the immune system without toxic doses.

↳ Silmitasertib is already in human trials, meaning this path could move fast.

This is a first step toward AI-assisted science, where models propose ideas, and labs validate them 🤖

Think “AlphaFold for biology,” but broader.

More importantly, we must remember that drug discovery takes 10–15 years and $2-3B per drug.

AI can cut that by half, or even more.

Open-sourcing the model on Hugging Face means any lab can build on this.

We’ve officially entered an era where AI isn’t just writing text. It’s writing science 🧬

Author

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