161–170 of 343 posts
Demis Hasabis has a really interesting and unusual CV for a nobel laureate [1], he started his career in AI game programming (he worked e.g. on Popoulous II, Syndicate, Theme Park for Bullfrog, and later for Lionhead Studios on Black & White) before doing a PhD in neuroscience, becoming an entrepreneur and starting DeepMind. I would say this is a refreshing and highly uncommon pick for a nobel prize, really cool to s…
Thus things like folding kinetics of transition states and intermediates, remain poorly understood through such statistical models, because they do not explicitly incorporate physical laws governing the protein system, such as electrostatic interactions, solvation effects, or entropy-driven conformational changes.
In particular, environmental effects are neglected - there's no modeling of the native solvated environment, where water molecules, ions, and temperature directly affect the protein’s conformational stability. This is critical when it comes to designing a novel protein with catalytic activity that's stable under conditions like high salt, high temperature etc.
As far as Nobel Prizes, it was already understood in the field two decades ago that no single person or small group was going to have an Einstein moment and 'solve protein folding', it's just too complicated. This award is questionable and the marketing effort involved by the relevant actors has been rather misleading - for one of the worst examples of this see:
https://www.scientificamerican.com/article/one-of-the-bigges...
For a more judicious explanation of why the claim that protein folding has been solved isn't really true:
"The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins" (June 2024)
I wasn't expecting to see David Baker in the list (just Demis and John). But I'm really glad to see it... David is a great guy. At CASP (the biannual protein structure prediction competition) around 2000, I sat down with David and told him that eventually machine learning would supplant humans at structure prediction (at the time Rosetta was already the leading structure prediction/design tool, but was filled with a…
For example, a very successful folding model had the signs reversed on hydrophobic and some electrostatic interactions. It made no sense physically but it gave a better prediction than competing models, and it was hard to move away from because it ranked well in CASP.
I wasn't expecting to see David Baker in the list (just Demis and John). But I'm really glad to see it... David is a great guy. At CASP (the biannual protein structure prediction competition) around 2000, I sat down with David and told him that eventually machine learning would supplant humans at structure prediction (at the time Rosetta was already the leading structure prediction/design tool, but was filled with a…
The models and tools designed for the CASP competition were an example of running around the solution space at a glacial pace and getting stuck in local minima. I can't speak for Rosetta by my labmates had fairly successful tools that usually ranked right behind Baker's lab, and they were plagued by issues where the most successful models had impossible or idiosyncratic terms in them. For example, a very successful f…
Yes, CASP was prone to getting stuck in local minima. I think the whole structure prediction field had become moribund.
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It’s fascinating how the affluent backgrounds of many famous scientists and entrepreneurs are downplayed. Eg Warren Buffet, Jeff Bezos, etc
If you gave the $300,000 Bezos got from his father to 10,000 random Americans in 1994, none of them would have created a company the equivalent of Amazon's scale.
I think I disagree with most of the comments here stating it’s premature to give the Nobel to AlphaFold. I’m in biotech academia and it has changed things already. Yes the protein folding problem isn’t “solved” but no problem in biology ever is. Comparing to previous bio/chem Nobel winners like Crispr, touch receptors, quantum dots, click chemistry, I do think AlphaFold already has reached sufficient level of impact.
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I would be very happy to be able to multiply my parents'/family's money by whatever factor Bezos, Gates or Musk have multiplied theirs.
The multiplier would be significantly different. Think the Wright Brothers first aeroplane vs a rocket ship.
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>> I do think AlphaFold already has reached sufficient level of impact. how so?
Well I'm sure one could look at number of published papers etc, but that metric is a lot to do with hype and I see it as a lagging indicator. A better one is seeing my grad-school friends with zero background in comp-sci or math, presenting their cell-biology results with AlphaFold in conferences and at lab meetings. They are not protein folding people either- just molecular biologists trying to present more evidence…
There's an on-point blog-post "AI and Biology" (https://www.science.org/content/blog-post/ai-and-biology) which illustrates why AlphaFold's real breakthrough is not super actionable for creating further bio-medicinal applications in a similar vein.
Earlier quoted context omitted.
It’s fascinating how the affluent backgrounds of many famous scientists and entrepreneurs are downplayed. Eg Warren Buffet, Jeff Bezos, etc
If you gave the $300,000 Bezos got from his father to 10,000 random Americans in 1994, none of them would have created a company the equivalent of Amazon's scale.
Earlier quoted context omitted.
It’s fascinating how the affluent backgrounds of many famous scientists and entrepreneurs are downplayed. Eg Warren Buffet, Jeff Bezos, etc
If you gave the $300,000 Bezos got from his father to 10,000 random Americans in 1994, none of them would have created a company the equivalent of Amazon's scale.
10k random Americans with backgrounds in software and a business idea? Not so clear.
You also seem very certain that Amazon's scale is a good thing, overall, which I remain unconvinced of.