We have partnered with Google DeepMind for this video to celebrate the launch of a follow up to
one of the best papers ever written, AlphaFold, now version 3 is here and I am out of words.
I’ll try my best to explain why what you are seeing here is history in the making.
So what is all this about? Well, this work is about protein folding and more,
you’ll see in a moment. A protein is a string of amino acids,
these are the building blocks of life. This is what goes in, which in reality, has a 3D
structure. And guessing that structure is protein folding. Letters go in, a 3D structure comes out.
That is brutally hard, and no one could produce a good enough solution before…until AlphaFold.
However, wait, a protein goes in, and this structure comes out…but what is all this good for?
Well, look at this follow up paper. This project was enabled by AlphaFold and is about
evolving enzymes. What kind of enzymes? Well, these are enzymes that are made to be able to
digest these plastics. Yes, that’s right. It is breaking these plastics down. But,
it gets better. It even helps recycling them, so they can be used over and over again,
infinitely. Like glass or tin cans. We don’t need to produce more of these,
which requires fossil fuels, and we can clean up these landfills with it. This sounds like
a science fiction movie, and it came right out of a paper that was published in 2021.
And this was just one citation. One follow up research paper that used this work. And,
hold on to your papers Fellow Scholars because it now has 21
thousand citations. Holy mother of papers! Just imagine all the ways this work already
changes the world and saves lives. And it gets better, that was only AlphaFold 2, and now,
AlphaFold 3 is here. And it just got even better. So, how could it possibly get better?
Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
So this just came out today,
and I had the honor of spending a little quality time with this paper earlier than its release,
and here is what I have found. By the way, this might be the first video you see on this anywhere.
So, it is better on proteins? Yes it is. It is marginally better in most categories, however,
on protein antibodies, its accuracy has more than doubled. That is an absolutely amazing
improvement worthy of a legendary paper all by itself. However, that is not all. Not even close!
This chart shows perhaps something even more insane.
Ligands are small molecules that interact with proteins - they can have complex
chemistry and huge amount of medical and biological research involves these,
in fact the majority of drugs are small molecules like these! There are specialist
physics based systems that try and predict the interactions of proteins and ligands,
and there are machine learning-based systems that tried to do the same thing too. Previously,
AlphaFold was limited to only proteins, and could not say anything about other molecular structures,
such as ligands. And…oh my goodness, what happened here? AlphaFold 3 can now not only
deal with these molecules too, but it also beats these specialist systems at their own
game! And not by a little. For the first time an AI-based system is outperforming the physics
based systems that are widely used in industry - this is crazy and it will change everything!
So this game is not just about proteins anymore. Today the 3D structure of ligands, ions,
and even DNA and RNA can also be predicted with the accuracy that is way beyond the
capabilities of previous methods. This new work can thus help develop biorenewable materials,
more resilient crops, accelerate drug design and genomics research. And once again, as we have seen
so many times recently, this is a multi-tasking AI that outperforms specialists. Like just recently
we saw SIMA, a technique that can play loads of games and outperforms specialist individual
game playing agents for each game - the way it does this is different from AlphaFold 3,
and it is playing across different environments, but my takeaway is the same: generalist AI models
are going to be better than specialist models at their own game. I am completely stunned by this.
So, what changes were necessary to pull this off? Well, a great deal has changed. For instance,
the previous version, AlphaFold2 had an Evoformer module that imagines this protein folding problem
as a graph. You can imagine such a graph as a social media website where each person is a
node in the graph, and they are connected if they know each other. However, here,
nodes represented the amino acid residues in the protein, these are the people, where the
connections represented their relationships, much like in the social media graph.
Now this Evoformer has been switched out to a new and simpler module called Pairformer. If
we pop the hood and look inside, we see this. We will talk a little more about this in a moment.
There is also a new diffusion module, somewhat similar to the ones that you see in text to image
models that start out from a bunch of noise, and over time, reorganize this noise to create
a beautiful image. However, here, instead, it creates 3D molecular structures. So cool!
These are the pieces that enable all this AI magic to happen. And this is
the corner of the internet where we look at bar charts and flip out together. Welcome to
Two Minute Papers. And this work is the start of a revolution. Wow. We now see that one single,
learning-based system over time can do more and more, and perhaps one day we will even have
just one AI that can perform every task for full drug discovery. This is a paper
that is likely already saving lives, and will save many more lives in the future.
Now, for this video, we have partnered with Google DeepMind. And of course,
we will still talk about the limitations of this technique, even more so than usual!
So what do I see here from the previous paper? What I see is not just a transformer neural
network being thrown at the problem. No-no. What I see is plenty of hand-engineering,
lots of little additions to the algorithm, each of which adds only a little more accuracy. No silver
bullet here…but lots of small moving parts. And the new version AlphaFold 3 also inherits many
of these moving parts. The new paper is a step in the right direction, it is a little simpler, but
I am hoping that over time, we will find a simpler and more elegant technique to perform these tasks.
Now, two more limitations. One, this can only predict static structures,
unfortunately it cannot capture more dynamical behaviors. Two,
the diffusion module starts out from a bunch of noise, and this noise is generated randomly,
and the technique shows a little sensitivity to that. This means that different runs
might provide different solutions, some of which will be less accurate. This can be
mitigated by running it a few more times from different starting points to improve accuracy.
And, one more thing! This is AlphaFold Server. A way for you to play with
AlphaFold 3 for free. If you are not a biologist, don’t worry for a second,
there are plenty of presets to choose from to have a look at. And it has everyone’s favorite,
dark mode! The link is available in the video description.
So now, I hope you Fellow Scholars also feel what I feel when reading this incredible paper,
and that is why I have a framed copy of the earlier AlphaFold paper in the
Two Minute Papers lab to remind me that we are living in a world where all this is possible.
What a time to be alive! And don’t forget, they are giving away all of this for free.
What an amazing gift to humanity! Also, what applications of AlphaFold would you
Fellow Scholars like to see in the world? Let me know in the comments below, I will
show the best ones to the authors and Google DeepMind. They’d love to hear your thoughts.