Nilay Patel

Out of the drug candidates that the model highlighted, only 10-30% were already known in prior literature.

The others had no prior link to the screen.

Interestingly, the model made a core prediction on how the family of drugs would function, which it used to base its result.

They wrote, What made this prediction so exciting was that it was a novel idea.

The model was generating a new testable hypothesis and not just repeating known facts.

Researchers then tested the hypothesis on actual cells and observed the predicted effect.

The model seems to have correctly identified a new way of turning tumorous cells hot under the desired conditions.

Google concluded, While this is an early first step, it provides a powerful experimentally validated lead for developing new combination therapies, which use multiple drugs in concert to achieve a more robust effect.

This result also provided a blueprint for a new kind of biological discovery.

It demonstrates that by following the scaling laws and building larger models like C2S scale 27B,

we can create predictive models of cellular behavior that are powerful enough to run high-throughput virtual screens, discover context-conditioned biology, and generate biologically grounded hypotheses.

One of the big implications here is that these larger science-specific models seem to actually have emergent capabilities in scientific reasoning, not just language-based reasoning.

To the extent that this is a bitter lesson outcome, i.e.

just the byproduct of a better, bigger, more dedicated model, that actually makes it more likely that this is a big unlock for future research rather than a one-off discovery.

Basically, the implications of there being a general scaling law for scientific reasoning models is quite large.

The reactions from many were excited.

We got, of course, the jokes.

Paki McCormick wrote, Everyone else, behold, an AI you can beat off to.

Google DeepMind, protein folding, weather prediction, new materials, and now an AI that can make its own cancer discoveries.

There was, however, some skepticism.