Hi, appear to the content, ColabFold and another variants of the same AlphaFold2: OmegaFold2 (is superiority in short sequences). However, ESM Fold is the fastest method for sequences with length 50 and 100. But, ESMFold it is not as accurate as Omega Fold or ColabFold. Both (Colab & Omega), are accurate methods than ESMFold. Still missing bring OpenFold.
Your "honestly I'm not entirely sure" at 7:53 speaks volumes... 😀 That's all fascinating and fine. But it remains an estimating tool that will always need empiric verification to check if the prediction is correct. And, at the end of the day, I doubt that, apart from theoretical new insights (yes, evolutionary biology might take great advantage of it). I don't think it will lead us to real-life practical applications such as curing diseases. Because we are light years away from filling the gap between protein structure and organic functions. We have no idea why a specific architecture leads to specific functionality. In fact, even once we will know all the protein structures, that will not automatically tell us how to design from it new drugs. Protein structure by itself will not be more informative to design new drugs as the mapping of the genome was for designing drugs against genetic diseases. As usual, again and again, it turns out that the map is not the territory. Anyway, thank you for updating us.
I think you misunderstood what I meant by that comment: I don't understand why Meta is working on this, they are a social network, VR, metaverse company. Meta's goal is to make money, I am not sure how this helps them increase revenue. The work itself is extremely useful and structures of proteins are extremely useful or many ways. And some proteins are very hard/impossible to determine experimentally.
@@AIMatej As far as I understand it, this explains in hindsight why an antibiotic works due to molecular structure. It was not the knowledge of this structure that led to its creation. I'm wondering if there is a case where the knowledge of a protein structure led to a new drug? After all this is what Alfafold is supposed to do in practical applications.
@@The-Wide-Angle Yes, pharmaceutical companies use protein structure all the time to design drugs. In a lot of cases, they produce drugs that target or attach to very specific proteins. You can simply login to your college library or use scihub to discover articles in which protein structure is targeted for drug binding, amongst many other use cases. Even before AI, we discovered protein structures and developed drugs and therapeutics to target those aspects. Go do some research and get off of TH-cam.
ok so what is the difference between these *Fold systems and the LLM's that have gotten so much attention in the last few weeks? are they different then the systems that can produce the most efficient structural designs for things like vehicles or furniture frames? what is Alpha Tensor? what other types of systems are there?
All of the models mentioned are based on the transformer architecture developed by google in 2017. But Alpha fold, ESM-2 fold and alpha tensor are very customized systems capable of amazing things, but they don't have a customer interface and are used for very specific purposes. The reason why ChatGPT/GPT-4 are getting so much press time is because normal people can try them out and the output is useful. So far I am not aware of anything that GPT-4 did that surpassed quality of human performance (but it is much faster). Alphafold and alphatensor did something that humans just could not do.
@@AIMatej These have a far more serious near-term application i.e. biology, but not a lot of people are even aware of them. The implications for basically every disease in existence and aging are immense. This year, a potential liver cancer drug was discovered in 30 days by AlphaFold.
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
![The moment we stopped understanding AI [AlexNet]](/img/tr.png)
Excellent. Thanks for making this informative video. You should have 100x the subscribers.
So nice of you to say!
Great job, Matej! You were always good at explaining things!
Thanks Omer! I hope that you are doing great!
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Great content, keep up the good work.
Appreciate it!
Excellent Content!
ESM-3 came out this year, 2024. The same year as AlphaFold 3.
Hi, appear to the content, ColabFold and another variants of the same AlphaFold2: OmegaFold2 (is superiority in short sequences).
However, ESM Fold is the fastest method for sequences with length 50 and 100. But, ESMFold it is not as accurate as Omega Fold or ColabFold. Both (Colab & Omega), are accurate methods than ESMFold.
Still missing bring OpenFold.
Thank you so much ☺️
You’re welcome 😊
Does this resolve the quality issues mentioned on the wiki page of protein folding that claims 1/3 of the data produced by AlphaFold was unusable?
what do you mean by "unusable"? low quality?
Your "honestly I'm not entirely sure" at 7:53 speaks volumes... 😀 That's all fascinating and fine. But it remains an estimating tool that will always need empiric verification to check if the prediction is correct. And, at the end of the day, I doubt that, apart from theoretical new insights (yes, evolutionary biology might take great advantage of it). I don't think it will lead us to real-life practical applications such as curing diseases. Because we are light years away from filling the gap between protein structure and organic functions. We have no idea why a specific architecture leads to specific functionality. In fact, even once we will know all the protein structures, that will not automatically tell us how to design from it new drugs. Protein structure by itself will not be more informative to design new drugs as the mapping of the genome was for designing drugs against genetic diseases. As usual, again and again, it turns out that the map is not the territory.
Anyway, thank you for updating us.
I think you misunderstood what I meant by that comment: I don't understand why Meta is working on this, they are a social network, VR, metaverse company. Meta's goal is to make money, I am not sure how this helps them increase revenue.
The work itself is extremely useful and structures of proteins are extremely useful or many ways. And some proteins are very hard/impossible to determine experimentally.
@@AIMatej Ok, can you make some examples of how the knowledge of the structure of proteins became useful?
@@The-Wide-Angle www.nature.com/articles/s42003-021-02261-4
@@AIMatej As far as I understand it, this explains in hindsight why an antibiotic works due to molecular structure. It was not the knowledge of this structure that led to its creation. I'm wondering if there is a case where the knowledge of a protein structure led to a new drug? After all this is what Alfafold is supposed to do in practical applications.
@@The-Wide-Angle Yes, pharmaceutical companies use protein structure all the time to design drugs. In a lot of cases, they produce drugs that target or attach to very specific proteins. You can simply login to your college library or use scihub to discover articles in which protein structure is targeted for drug binding, amongst many other use cases. Even before AI, we discovered protein structures and developed drugs and therapeutics to target those aspects. Go do some research and get off of TH-cam.
ok so what is the difference between these *Fold systems and the LLM's that have gotten so much attention in the last few weeks? are they different then the systems that can produce the most efficient structural designs for things like vehicles or furniture frames? what is Alpha Tensor? what other types of systems are there?
All of the models mentioned are based on the transformer architecture developed by google in 2017. But Alpha fold, ESM-2 fold and alpha tensor are very customized systems capable of amazing things, but they don't have a customer interface and are used for very specific purposes.
The reason why ChatGPT/GPT-4 are getting so much press time is because normal people can try them out and the output is useful. So far I am not aware of anything that GPT-4 did that surpassed quality of human performance (but it is much faster).
Alphafold and alphatensor did something that humans just could not do.
@@AIMatej These have a far more serious near-term application i.e. biology, but not a lot of people are even aware of them. The implications for basically every disease in existence and aging are immense. This year, a potential liver cancer drug was discovered in 30 days by AlphaFold.
this is also is why people worry about vaccines.