Nilay Patel

So today we're going to talk about first this particular discovery, and then more broadly how quietly, behind all the hype and noise, there have been some really interesting advancements which suggest that the whole idea that AI can't make or contribute to novel discoveries in science may be one now for the junk heap of history.

So back to this discovery from Google.

In their announcement post, Google wrote, this announcement marks a milestone for AI and science.

C2S scale generated a novel hypothesis about cancer cellular behavior, and we have since confirmed its prediction with experimental validation in living cells.

The implications, they say, are new pathways for developing therapies to fight cancer.

Now, they explain that one of the biggest challenges in cancer therapy is that many tumors are, quote unquote, cold.

In other words, invisible to the body's immune system.

A major strategy in cancer treatment, then, is triggering tumorous cells to make them turn hot, i.e., to display immune-triggering signals, in a process that's called antigen presentation.

With this as background, researchers gave C2S scale a single task.

to find a drug that functions as a conditional amplifier.

In other words, to boost the immune signal only in specific circumstances.

Previous iterations of similar models were not capable of achieving this task, but C2S scale succeeded.

The task effectively required a sort of conditional biological reasoning.

They designed what they called a dual-context virtual screen, where they, one, provided the model with real-world patient samples with intact tumor-immune interactions,

and low-level interferon signaling, and then secondly provided the model with isolated cell line data with no immune context.

Google then simulated the effects of over 4,000 drugs and asked the model to predict which would boost immune signals if only certain conditions were met.

Now, this highlights one of the areas where we're seeing AI-enhanced science really flourish.

AI models generally excel in situations where a large volume of experimentation is required.

In other words, a big part of the value is about speeding through simulated experiments and crunching large datasets that would take human researchers and traditional computing methods much, much longer to sift through.

After simulating those 4,000 drugs, the experiment found a set of drug candidates.