Deep dive into the AlphaFold 3 algorithm
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- เผยแพร่เมื่อ 16 ก.ย. 2024
- This video is a long and detailed description of AlphaFold 3. I've been spending a lot of time understanding the algorithm and this video is the result of that. But if you just want to get the gist of the algorithm then I'd recommend these much shorter videos I've made:
AlphaFold 2: • How AlphaFold solves p...
AlphaFold 3: • What’s changed in Alph...
Here are the notes I was writing in the video: lookingglassun...
![HIWWHEE | OHM [Official MV]](http://i.ytimg.com/vi/OJJLtTLqLag/mqdefault.jpg)
This video is very long and detailed, so please don't feel you have to watch this one. I've been spending a lot of time understanding the AlphaFold 3 algorithm and this video is the result of that. But if you just want to get the gist of the algorithm then I'd recommend these much shorter videos I've made:
AlphaFold 2: th-cam.com/video/3gSy_yN9YBo/w-d-xo.html
AlphaFold 3: th-cam.com/video/CYncNBMPLLk/w-d-xo.html
Here are the notes I was writing in the video: lookingglassuniverse.substack.com/p/deep-dive-into-alphafold-3
Pretty inspiring that you studied it enough to be able to explain it in an approachable way (speaking for myself anyway). How long would you say you spent on this learning project overall?
Thank you!! About 2 months, but some of that time was learning other relevant algorithms like diffusion
Yup. But I mean I first learnt about more general diffusion models etc before looking into the specifics in AlphaFold
Very nice description of AF3 - you did a great job describing this important finding for us structural biologists who are not well versed in AI. If you have time, a follow-up on the ways they assess the reliability of their models would be great. Thanks!
cryoEM rocks
A really one video i was looking to get a broad picture overview of the alphafold algorithm , i watch several other video but no can has included the details in that depth,
thanks once again i really appreciated your work and time, its bit challenging to understad at very first time.
Wow this was a great video, despite not understanding all of what was going on and the terms and processes I saw on screen, I felt that you explained it really well and made it accessible to someone who has not previously delved deeply into any sort of "AI" process. Thanks so much!
So, if it is predicting the position of the molecules well does that imply that it must have picked up from the training data a heuristic for physics? Surely whatever it learned wasn't a conventional molecule simulation, otherwise the results would be the same. Could we poke its insides and find out if it has learned at least things like 'opposite charges attract'? Is this something that is talked about in those papers at all? Or is this a case where it really isn't known exactly what it is learning but it IS working(!)? I wonder if it'd be possible to dissect the parts of physics alphafold thought to be the most relevant and then apply it to a "barebones" molecule simulation. Thanks for the video
Such a great idea! I would love to know what exactly it’s learning. You’d assume it’s at least some physics/ chemistry! But it also learns how to incorporate the templates and the evolutionary data. It’d be so interesting to find out what it learns, since it does better than pure physics simulations
Barebones is possible with ab initio and dft related methods. cryoEM has pushed microscopy to next level so enrichment of data is very much possible but one no longer depends on alpha fold a lot but writes their own custom models over publicly funded protein databases
Please make more deep dive videos. Amazing video, thanks!
A day with a new video by Mithuna is always a GOOD day.
These detailed deep dives are just the best!
I have a genius autistic son, maths prodigy etc, and we always look forward to new vids. Sending thanks for all your hard work.
Thank you for the lovely lovely message!!
This is fun, I started studying maths, switched to a biology heavy BSc and MSc but ended as software dev and this is the perfect combination of all three fields.
Epigenetic attractor network based medicines and cryoEM rocks
Interesting to see that the diffusion module basically works the same as a picture generator like dall-E. I didn't see that coming.
Me neither!
cryoEM also has some very simplistic image improvement techniques, besides offering whatever we were waiting for at almost atomic resolution. Now dft and ab initio + hpc/gpu can do some massive simulations hopefully soon
What, another Looking Glass video, you are on a roll. 🙂
Hey I'm 16F
Saw your video bout being mad at math..then going on to learn math in uni and wanting to be good at it .
I'm the same and found the video very relatable to my present condition.
You have really inspired and motivated me and as a fellow women in stem thank youuu❤
That was amazing. Please do more deep dives like this.
perfect presentation！It would be great if u can introduce more details about the training part
Just found this incredible channel! I found your video on understanding the codebase of OpenFold so relatable. I recently tackled the same, but didn’t think to use Claude. Did you get through the whole code by now? I just started a TH-cam series on implementing AlphaFold 2 from scratch (code’s ready, videos hopefully by October). I feel like our interests are super aligned right now!
That’s awesome!! I’d love to see that when you’ve done it!
This was a tuff video to get through. I maybe got like 20-30% of it. AlphaFold need not worry that I will create a competing algorithm right away 😂
I understand that the triangle inequality is important for alignment in 3D but how is it actually carried out in the algorithmic level? In regular attention I guess you have an nxn matrix but here you have to represent nxnxn? Is that the way it works?
Thanks for the new video!
This is so informative thank you so much for making this!!!
Do you think this technology is on track to model entire cells and eventually organisms? If not, why not? What will it take to get us to that point?
Thank you for the super thanks! That's very kind of you!
I think that is the goal for sure, and there's at least one company I've heard of claim that they've done it (www.nature.com/articles/d43747-022-00108-3). I can't say how successful that's been, but I think it will eventually be possible to do. Cells are much much much more complicated than a single protein though.
cryoEM, dft and ab initio + ai/ml is the way to go. The human genius of maths is that an mit mathematician has modelled fractal structure of chromatin bundles in nucleus as a fractal. Then proven correct and the ideal state can be a good symmetry check. Chromatin bundles act like attractor networks. Sometimes we needlessly worry about broken and empty places while it could also imply adaptation is change of surface folds and orientation. Helmholtz attractors have flexibility issues that's why we got a brain and neurons to teach our cells by counterfactual evidence. Epigenetics is back. Waddington drew a network below the landscape and that's big deal. You can also auto heal. But also create intelligent medicine
@@zephyrlibs what do you think about Penrose’s theory that microtubules in the cells are components of a vast, entangled quantum processor representing the true intelligence of cells, especially cell morphologies?
Thanks for the great content!
Question a bit out of context: what app are you using?
It’s Goodnotes for the iPad!
@@LookingGlassUniverse Thanks!
What math do you need to make an alphafold
I feel bad i didnt watch this sooner
This is awesome, and I do DNA/RNA/MRNA/Ribosomal/Dendrite reacted research of chain reactions//// often.//// in that pattern of cause and effect as the ball rolls down the steps of order and trackitude on its path and course./... anyways... you get it lol//. Enough said for that//////. What do you think about the MRNA Vaccine..... would the Ribosomal tampering or mingling be rendered a mutation on the dendrite? Was the dendrite its self objectively mutated therefore? Or is this different hap stance a coat that can be shed? Later on?? As the cells replicate thru there natural cycles.... of renewal.? Plz reply If you know. Thx and stay learning things..... I see you're interested in things like sciences and the way things are and such.... that's awesome. I admire that/
cryoEM and biophysics. Reading through 1800-2024 is what I like. Including mediaeval paintings, religion and what not to form a grand veiw
Adhd is super power. Google books is awesome. Autism makes me penny poor but 3 years some fun experiments and mountain climbing on my epigenetic landscape networks to auto alter them
Checking Peschek's paper - life is work - meta stability, cyanobacteria origins of life
Bioenergetic Processes of Cyanobacteria: From Evolutionary Singularity to Ecological Diversity
Life Implies Work: A Holistic Account of Our Microbial Biosphere Focussing on the Bioenergetic Processes of Cyanobacteria, the Ecologically Most Successful Organisms on Our Earth
Wooooow! You are smart!!!!
beeter hi jay
Why are u into these kinda stuffs? Thought you're Quantum Computing kinda gal?
people's interests can change/evolve
People used to say that protein folding was one of the biggest uses for quantum computing, so now we can all think about other cool computing stuff!
Specially quantum chemistry
Protein folding problem finally solved ?
cryoEM
Dft and ab initio remain in the simulation pipeline
Isn't it interesting that a protein can fold itself up in a nanosecond ?
Point being, explain the gap between 100,000 PS3s running 'Fold At Home' all week and a nanosecond.
There's a deep point there... A fundamental truth. Some won't get it.
I can't explain it, but I see it there screaming at us.
I guess light harvesting complex proteins don't do too much of this