AlphaFold 3 predicts the structure and interactions of life's molecules

Post by goggy_googy » Wed, May 08, 2024, 5:12 PM UTC

philip1209 wrote: post #78

moconnor wrote: post #32

Stepping 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 makes me think about how Einstein was famous for making falsifiable real-world predictions to accompany his theoretical work. And, sometimes it took years for proper experiments to be run (such as measuring a solar eclipse during the breakout of a world war). Perhaps the opportunity here is to provide a quicker feedback loop for theory about predictions in the real world. Almost like unit tests.

Post by mathgradthrow » Wed, May 08, 2024, 5:13 PM UTC

_yb2s wrote: post #120

moconnor wrote: post #32

Stepping 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 means we now have an accurate surrogate model or "digital twin" that can be experimented on almost instantaneously. So we can massively accelerate the traditional process of developing mechanistic understanding through experiment, while also immediately be able to benefit from the ability to make accurate predictions, even without needing understanding. In reality, science has already pretty much gone this way lon…

Post by tnias23 » Wed, May 08, 2024, 5:13 PM UTC

moconnor wrote: post #32

Stepping 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…

Post by Laaas » Wed, May 08, 2024, 5:14 PM UTC

dopylitty wrote: post #13

This reminds me of Google’s claim that another “AI” discovered millions of new materials. The results turned out to be a lot of useless noise but that was only apparent after actual expert spent hundreds of hours reviewed the results[0] 0: https://www.404media.co/google-says-it-discovered-millions-o...

Post by pantalaimon » Wed, May 08, 2024, 5:15 PM UTC

mhrmsn wrote: post #24

Metacelsus wrote: post #4

From: 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.