If a simple majority classifier has the same performance as a fancy model with 58 layers of transformers, and you use your fancy model instead of the majority classifier, is it the model that's doing the discovery or is it the operator that choose to look in a particular place?
I am all for crediting humans and I don't particularly fancy all the anthropomorphising myself. However rubbing it in now feels similarly pointless as suggesting the US should switch to metric.
Well it's important, because the particular new lead for drug targeting is not super valuable, they are a dime a dozen, easier to find than a startup idea. Actually driving a successful drug development program is an entirely different matter that can only be established with $10-$100M of early exploration, with a successful drug costing much more to get to market.
It could also be that particular prioritization method that uses Gemma is useful in its own, but we won't know that unless it is somehow benchmarked against the many alternatives that have been used up until now. And in other benchmark settings, these cell sentence methods have not been that impressive.