Imagine the goodwill for humanity for releasing these pure research systems for free. I just have a hard time understanding how you can motivate to keep it closed. Let's hope it will be replicated by someone who doesn't have to hide behind the "responsible AI" curtain as it seems they are now.
Are they really thinking that someone who needs to predict 11 structures per day are more likely to be a nefarious evil protein guy than someone who predicts 10 structures a day? Was AlphaFold-2 (that was open-sourced) used by evil researchers?
The entire point[0] is that they want to sell an API to drug-developer labs, at exclusive-monopoly pricing. Those labs in turn discover life-saving drugs, and recoup their costs from e.g. parents of otherwise-terminally-ill children—again, priced as an exclusive monopoly.
[0] As signaled by "it is not possible to obtain structures of proteins bound to possible drugs"
It's a massive windfall for Alphabet, and it'd be a profound breach of their fiduciary duties as a public company to do anything other than lock-down and hoard this API, and squeeze it for every last billion.
This is a deeply, deeply, deeply broken situation.
I agree that late-stage capitalism can create really tough situations for poor families trying to afford drugs. At the same time, I don't know any other incentive structure that would have brought us a breakthrough like AlphaFold this soon. For the first time in history, we have ML models that are beating out the scientific models by huge margins. The very fact that this comes out of the richest, most competitive country in the history of the world is not a coincidence.
The proximate cause of the suffering for terminally-ill children is really the drug company's pricing. If you want to regulate this, though, you'll almost certainly have fewer breakthroughs like AlphaFold. From a utilitarian perspective, by preserving the existing incentive structure (the "deeply broken situation" as you call it), you will be extending the lifespans of more people in the future (as opposed to extending lifespans of more people now by lowering drug prices).
AlphaFold wasn't some lone genius breakthrough that came out of nowhere, everything but the final steps were basically created in academia through public funding. The key insights, some combination of realizing that the importance of sequence to structure to function put analyzable constraints on sequence conservation and which ML models could be applied to this, were made in academia a long time ago. AlphaFold's training set, the PDB, is also a result of decades of publicly funded work. After that, the problem was just getting enough funding amidst funding cuts and inflation to optimize. David Baker at IPD did so relatively successfully, Jinbo Xu is less of a fundraiser but was able to keep up basically alone with one or two grad students at a time, etc. AlphaFold1 threw way more people and money to basically copy what Jinbo Xu had already done and barely beat him at that year's CASP. Academics were leading the way until very, very recently, it's not like the problem was stalled for decades.
Thankfully, the funding cuts will continue until research improves, and after decades of inflation cutting into grants, we are being rewarded by funding cuts to almost every major funding body this year. I pledge allegiance to the flag!
EDIT: Basically, if you know any scientists, you know the vast majority of us work for years with little consideration for profit because we care about the science and its social impact. It's grating for the community, after being treated worse every year, to then see all the final credit go to people or companies like Eric Lander and Google. Then everyone has to start over, pick some new niche that everyone thinks is impossible, only to worry about losing it when someone begins to get it to work.
My frustrations aren't with a lack of open source models, some poor souls make them. My disagreement is with the perception that academia has insufficient incentive to work on socially important problems. Most such problems are ONLY worked on in academia until they near the finish line. Look at Omar Yaghi's lab's work on COFs and MOFs for carbon/emission sequestration and atmospheric water harvesting. Look at all the thankless work numerous labs did on CRISPR-Cas9 before the Broad Institute even touched it. Look at Jinbo Xu's work, on David Baker's lab's and the IPD's work, etc. Look at what labs first solved critical amyloid structures, infuriatingly recently, considering the massive negative social impacts of neurodegenerative diseases.
It's only rational for companies that only care about their own profit maximization to socialize R&D costs and privatize any possible gains. This can work if companies aren't being run by absolute ghouls who aren't delaying the release of a new generation of drugs to minimize patent duration overlap or who aren't trying to push things that don't work for short-term profit. This can also work if we properly fund and credit publicly funded academic labs. This is not what's happening, however, instead public funded research is increasingly demeaned, defunded, and dismantled due to the false impression that nothing socially valuable gets done without a profit motive. It's okay, though, I guess under this kind of LSC worldview, that everything always corrects itself so preempting problems doesn't matter, we'll finally learn how much actual innovation is publicly funded when we get the Minions movie, aducanumab, and WeWork over and over again for a few decades while strangling the last bit of nature we have left.
We're making good progress but it's difficult to interface with fundamentally different organizational models between academia and industry. I'm hoping that this model will become normalized in the future. But it takes serious leaps of faith from all involved (professors, industry leaders, grant agencies, and - if I can flatter myself - early career scientists) to leave the "safe route" in their organizations and try something like this.
Well, allegedly.
It's not like Google is ever going to make billions on this anyway, the alphafold algorithms are not super advanced and you don't require the datasets of gpt4 to train them so others will hopefully catch up.. though I'm also pretty sure it requires GPU-hours beyond what a typical non-profit academia outfit has available unfortunately.. :/
> AlphaFold 3 will be available as a non-commercial usage only server at https://www.alphafoldserver.com, with restrictions on allowed ligands and covalent modifications. Pseudocode describing the algorithms is available in the Supplementary Information. Code is not provided.
I can image training will be expensive, but I don't think it will be at a GPT-4 level of expensive.
I really have to emphasize that transformers have literally transformed science in only a few years. Truly extraordinary.
This is yet another large part of a biotech related Gutenberg moment.
There is a biennial (biannual?) competition known as CASP where some new structures, not yet published, are used for testing predictions from a wide range of protein structure prediction (so, basically blind predictions which are then compared when the competition wraps up). AlphaFold beat all the competitors by a very wide margin (much larger than the regular rate of improvement in the competition), and within a couple years, the leading academic groups adopted the same techniques and caught up.
It was one of the most important and satisfying moments in structure prediction in the past two+ decades. The community was a bit skeptical but as it's been repeatedly tested, validated, and reproduced, people are generally of the opinion that DeepMind "solved" protein structure prediction (with some notable exceptions), and did so without having the solve the full "protein folding problem" (which is actually great news while also being somewhat depressing).
I have a history to tell for the record, back in the 90s we developed a home banking for Palm (with a modem), it was impossible to perform RSA because of the speed so I contacted the CEO of Certicom which was the unique elliptic curve cryptography implementation at that time. Fast forward and ECC is everywhere.
Thus if you can design a viral coat protein to bind to a human cell-surface receptor, such that it gets translocated into the cell, then it doesn't matter so much where that virus came from. The cell's firewall against viruses is the cell membrane, and once inside, the biomolecular replication machinery is very similar from species to species, particularly within restricted domains, such as all mammals.
Thus viruses from rats, mice, bats... aren't going to have major problems replicating in their new host - a host they only gained access to because some nation-state actors working in collaboration on such gain-of-function research in at least two labs on opposite sides of the world with funds and material provided by the two largest economic powers for reasons that are still rather opaque, though suspiciously banal...
Now while you don't need something like AlphaFold3 to do recklessly stupid things (you could use directed evolution, making millions of mutatad proteins, throwing them at a wall of human cell receptors and collecting what stuck), it makes it far easier. Thus Google doesn't want to be seen as enabling, though given their prediliction for classified military-industrial contracting to a variety of nation-states, particularly with AI, with revenue now far more important than silly "don't be evil" statements, they might bear watching.
On the positive side, AlphaFold3 will be great for fields like small molecular biocatalysis, i.e. industrial applications in which protein enzymes (or more robust heterogenous catalysts designed based on protein structures) convert N2 to ammonia, methane to methanol, or selectively bind CO2 for carbon capture, modification of simple sugars and amino acids, etc.
Google has no such restriction.
There was a nervous chuckle from the audience.
Science advances because of an open exchange of ideas, the original idea of patents was to grant the inventor exclusive use in exchange for disclosure of knowledge.
Those who did not patent, had to accept that their inventions would be studied and reverse engineered.
The „as a service“ model, breaks that approach.
>Unlike RoseTTAFold and AlphaFold2, scientists will not be able to run their own version of AlphaFold3, nor will the code underlying AlphaFold3 or other information obtained after training the model be made public. Instead, researchers will have access to an ‘AlphaFold3 server’, on which they can input their protein sequence of choice, alongside a selection of accessory molecules. [. . .] Scientists are currently restricted to 10 predictions per day, and it is not possible to obtain structures of proteins bound to possible drugs.
This is unfortunate. I wonder how long until David Baker's lab upgrades RoseTTAFold to catch up.