[talldayo]

> Nvidia also need to invent smth then, as pumping mining (or giving good to gamers) again is not sexy.

They do! Their research page is well worth checking out, they wrote a lot of the fundamental papers that people cite for machine learning today: https://research.nvidia.com/publications

> Will we finally compute for drug development and achieve just as great results as with chatbots?

Maybe - but they're not really analogous problem spaces. Fooling humans with text is easy - Markov chains have been doing it for decades. Automating the discovery of drugs and research of proteins is not quite so easy, rudimentary attempts like Folding@Home went on for years without any breakthrough discoveries. It's going to take a lot more research before we get to ChatGPT levels of success. But tools like CUDA certainly help with this by providing flexible compute that's easy to scale.

[dekhn]

There was nothing rudimentary about Folding@Home (either in the MD engine or the MSM clustering method), and my paper on GPCRs that used Folding@Home regularly gets cites from pharma (we helped establish the idea that treating proteins as being a single structure at the global energy minimum was too simplistic to design drugs). But F@H was never really a serious attempt at drug discovery- it was intended to probe the underlying physics of protein folding, which is tangentially related.

In drug discovery, we'd love to be able to show that virtual screening really worked- if you could do docking against a protein to find good leads affordably, and also ensure that the resulting leads were likely to pass FDA review (IE, effective and non-toxic), that could potentially greatly increase the rate of discovery.