I'm interested in how they measure accuracy of binding site identification and binding pose prediction. This was missing for the hitherto widely-used binding pose prediction tool Autodock Vina (and in silico binding pose tools in general). Despite the time I invested in learning & exercising that tool, I avoided using it for published research because I could not credibly cite its general-use accuracy. Is / will Alphafold 3 be citeable in the sense of "I have run Alphafold on this particular target of interest and this array of ligands, and have found these poses of X kJ/mol binding energy, and this is known to an accuracy of Y% because of Alphafold 3's training set results cited below'
AlphaFold 3 predicts the structure and interactions of life's molecules
281–290 of 504 posts
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#282Earlier quoted context omitted.
- "Imagine the goodwill for humanity for releasing these pure research systems for free." 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 structur…
Is it broken if it yields new drugs? Is there a system that yields more? The whole point of capitalism is that it incentivizes this in a way that no other system does.
My point one level up in the comments here, was not really that the system is broken, but more like asking how you can run these companies (google and that other part run by the deepmind founder, who I bet already has more money than he can ever spend) and still sleep well knowing you're the rich capitalist a-hole commercializing life-science work that your parent company has allocated maybe one part in a million of their R&D budget into creating.
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.. :/
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#283Earlier quoted context omitted.
Consider that humans also learn from other humans, and sometimes surpass their teachers. A bit more comfortable?
Ahh, but the new young master is able to explain their work and processes to the satisfaction of the old masters. In the 'Science' of our modern times it's a requirement to show your work (yes, yes, I know about the replication crisis and all that terrible jazz). Not being able to ascertain how and why the ML/AI is achieving results is not quite the same and more akin to the alchemists and sorcerers with their cypher…
> the new young master is able to explain their work and processes to the satisfaction of the old masters
Yes, but it's one level deep - in general they wouldn't be able to explain their work to their master's master (note "science advances one funeral at a time").
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#284Earlier quoted context omitted.
What if our understanding of the laws of the natural sciences are subtly flawed and AI just corrects perfectly for our flawed understanding without telling us what the error in our theory was? Forget trying to understand dark matter. Just use this model to correct for how the universe works. What is actually wrong with our current model and if dark matter exists or not or something else is causing things doesn't matt…
ML is accustomed with the idea that all models are bad, and there are ways to test how good or bad they are. It's all approximations and imperfect representations, but they can be good enough for some applications. If you think carefully humans operate in the same regime. Our concepts are all like that - imperfect, approximative, glossing over some details. Our fundamental grounding and test is survival, an unforgivi…
Also lax enough for the hilarious mismanagement of the situation by "the experts". At least anti-vaxxers have an excuse.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#285Stepping 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…
As a steelman, wouldn't the abundance of infinitely generate-able situations make it _easier_ for us to develop strong theories and models? The bottleneck has always been data. You have to do expensive work in the real world and accurately measure it before you can start fitting lines to it. If we were to birth an e.g. atomically accurate ML model of quantum physics, I bet it wouldn't take long until we have mathemat…
This is an important aspect that's being ignored IMO.
For a lot of problems, currently you either don't have an an analytical solution and the alternative is a brute force-ish numerical approach. As a result the computational cost of simulating things enough times to be able to detect behavior that can inform theories/models (potentially yielding a good analytical result) is not viable.
In this regard, ML models are promising.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#286Earlier quoted context omitted.
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…
Medicine can be explained fairly simply, and the why of how it works as it does is also explained by this: Imagine a very large room that has every surface covered by on-off switches. We cannot see inside of this room. We cannot see the switches. We cannot fit inside of this room, but a toddler fits through the tiny opening leading into the room. The toddler cannot reach the switches, so we equip the toddler with a p…
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Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#287Probably worth mentioning that David Baker’s lab released a similar model (predicts protein structure along with bound DNA and ligands), just a couple of months ago, and it is open source [1]. It’s also worth remembering that it was David Baker who originally came up with the idea of extending AlphaFold from predicting just proteins to predicting ligands as well [2]. 1. https://github.com/baker-laboratory/RoseTTAFold…
And that tech just got $1b in funding.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#288I'm interested in how they measure accuracy of binding site identification and binding pose prediction. This was missing for the hitherto widely-used binding pose prediction tool Autodock Vina (and in silico binding pose tools in general). Despite the time I invested in learning & exercising that tool, I avoided using it for published research because I could not credibly cite its general-use accuracy. Is / will Alph…
I've never trusted those predicted binding energies. If you have predicted a ligand/protein complex and have high confidence in it and want to study the binding energy I really think you should do a full MD simulation, you can pull the ligand-protein complex apart and measure the change in free energy explicitly.
Also, and this is an unfounded guess only, the problem of protein / ligand docking is quite a bit more complex than protein folding - there seems to be a finite set of overall folds used in nature, while docking a small ligand to a big protein with flexible sidechains and even flexible large-scale structures can have induced fits that are really important to know and estimate, and I'm just very sceptical that it's going to be possible to in a general fashion ever predict these accurately by the AI model with the limited training data.
Though you just need some hints, then you can run MD sims on them to see what happens for real.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#289Earlier quoted context omitted.
Accuracy can be assessed two main ways: computationally and experimentally. Computationally, they would compare the predicted structures and interactions with known data from databases like PDB (Protein Database). Experimentally, they can use tools like x-ray crystallography and NMR (nuclear magnetic resonance) to obtain the actual molecule structure and compare it to the predicted result. The outcomes of each approa…
AlphaFold very explicitly (unless something has changed) removes NMR structures as references because they are not accurate enough. I have a PhD in NMR biomolecular structure and I wouldn't trust. the structures for anything.
interesting observation and experience. must have made thesis development complex, assuming the realization dawned on you during the phd.
what do you trust more than NMR?
AF's dependence on MSAs also seems sub-optimal; curious to hear your thoughts?
that said, it's understandable why they used MSAs, even if it seems to hint at winning CASP more than developing a generalizable model.
arguably, MSA-dependence is the wise choice for early prediction models as demonstrated by widespread accolades and adoption, i.e., it's an MVP with known limitations as they build toward sophisticated approaches.
Re: AlphaFold 3 predicts the structure and interactions of life's molecules
#290Earlier quoted context omitted.
If you're a scientist who works in protein folding (or one of those other areas) and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing, especially if you aren't proficient enough with ML to reproduce this work in your own hands. If you're a scientist who accepts that probabilist models beat interpretable ones (articulated well here: https://no…
The goal of science has always been to discover underlying principles and not merely to predict the outcome of experiments. I don't see any way to classify an opaque ML model as a scientific artifact since by definition it can't reveal the underlying principles. Maybe one could claim the ML model itself is the scientist and everyone else is just feeding it data. I doubt human scientists would be comfortable with that…
What if it turns out that nature simply doesn't have nice, neat models that humans can comprehend for many observable phenomena?