I’m inclined to ignore such pr fluff until they actually demonstrate a _practical_ result. Eg. cure some form of cancer or some autoimmune disease. All this “prediction of structure” has been in the news for years, and it seems to have resulted in nothing practically usable IRL as far as I can tell. I could be wrong of course, I do not work in this field
AlphaFold 3 predicts the structure and interactions of life's molecules
81–90 of 504 posts
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#82Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…
The ML models will help us understand that :)
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#83Earlier quoted context omitted.
The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. The work on alphafold will likely net Demis and John a Nobel prize in the next few years. (that said, one should always inspect Google publications with a fine-toothed comb and lots of skepticism, as they have a tendency to juice the results)
>The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. This is clearly an overstatement, or at least very incomplete. See for instance https://www.nature.com/articles/s41592-023-02087-4 : "In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differ…
Yep, I know Paul Adams (used to work with him at Berkeley Lab) and that's exactly the paper he'd publish. If you read that paper carefully (as we all have, since it's the strongest we've seen from the crystallography community so far) they're basically saying the results from AF are absolutely excellent, and fit for purpose.
(put another way: if Paul publishes a paper saying your structure predictions have issues, and mostly finds tiny local issues and some distortion and domain orientation,r ather than absolutely incorrect fold prediction, it means your technique works really well, and people are just quibbling about details.)
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#84I am trying to understand how accurate the docking predictions are.
Looking at the PoseBusters paper [1] they mention, they say they are 50% more accurate than traditional methods.
DiffDock, which is the best DL based systems gets 30-70% depending on the dataset, and traditional gets 50-70%. The paper highlighted some issues with the DL-based methods and given that DeepMind would have had time to incorporate this into their work and develop with the PoseBusters paper in mind, I'd hope it's significantly better than 50-70%. They say 50% better than traditional so I expected something like 70-85% across all datasets.
I hope a paper will appear soon to illuminate these and other details.
[1] https://pubs.rsc.org/en/content/articlehtml/2024/sc/d3sc0418...
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#85From: https://www.nature.com/articles/d41586-024-01383-z >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…
Also no commercial use, from the paper: > 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.
How easy/hard would be for the scientific community to come up with an "OpenFold" model which is pretty much AF3 but fully open source and without restrictions in it?
I can image training will be expensive, but I don't think it will be at a GPT-4 level of expensive.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#86Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…
every time the two systems disagree, it's an opportunity to learn something. both kinds of models can be improved with new information, done through real-world experiments
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#87Earlier quoted context omitted.
My argument is: weather. I think it is fine & better for society to have applications and models for things we don't fully understand... We can model lots of small aspects of weather, and we have a lot of factors nailed down, but not necessarily all the interactions.. and not all of the factors. (Additional example for the same reason: Gravity) Used responsibly. Of course. I wouldn't think an AI model designing an ai…
It would be cool to see an airplane made using generative design.
How about spaceship parts ? https://www.nasa.gov/technology/goddard-tech/nasa-turns-to-a...
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#88Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…
It depends whether the value of science is human understanding or pure prediction. In some realms (for drug discovery, and other situations where we just need an answer and know what works and what doesn’t), pure prediction is all we really need. But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics? Maybe there’ll be an inter…
> But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics?
At that point I wonder if it would be possible to feed that uninterpretable model back into another model that makes sense of it all and outputs sets of equations that humans could understand.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#89Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-int…
Some machine learning models might be more interpretable than others. I think the recent "KAN" model might be a step forward.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#90From: https://www.nature.com/articles/d41586-024-01383-z >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…
Also no commercial use, from the paper: > 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.
Yes, because that's going to stop competitors.. it's why they didn't release code I guess.
This is yet another large part of a biotech related Gutenberg moment.