Googles ALPHAFOLD-3 Just Changed EVERYTHING! (AlphaFold 3 Explained)

So Google DeepMind and Isomorphic Labs have just surprised the industry by releasing AlphaFold 3 and announcing how good it truly is. So it says AlphaFold3 predicts the structure and interactions of all of life's molecules. You can see here it states, introducing AlphaFold 3, a new AI model developed by Google DeepMind and Isomorphic Labs. By accurately predicting the structure of proteins, DNA, RNA ligands and more, and how they interact, we hope it will transform our understanding of the biological world and drug discovery. So you have to think about it like this. In every cell of every living thing, plant animals, even us, there are literally billions of these microscopic machines. And these machines are made up of proteins, DNA and other funky molecules. And the thing is, is that none of these pieces work alone. They're all kind of interacting, kind of combining in millions of ways. And only by seeing that interaction can we actually understand how life works. And this is where AlphaFold 3 comes in. They literally just published a pager in Nature magazine which we'll get into later, in which they talk about this amazing new AI model. And this model can literally predict the structure of life's very molecules and even how they interact with crazy good accuracy. And we're actually talking a significant leap on what we could do before. And the crazy thing is, is that this is probably about to show us how all those crazy, crazy machines fit together, why they behave the way they do. And this is the key to understanding everything from diseases to how plants grow and how to fix problems or build new awesome stuff. And the details of this are pretty, pretty crazy. And it's pretty incredible that they've managed to build upon the success of AlphaFold 2, which was the one that changed the game in understand proteins. And researchers everywhere actually do use it. Think malaria vaccines, cancer research, the entire whole deal. And AlphaFold's already been such a game changer, it's even won major Science Awards. And AlphaFold3, it actually goes way beyond just proteins to all sorts of biomolecules. Think eco friendly materials, stronger crops, supercharged medicine. This is pretty much next level science with the power to change the world. So, so how does this actually work? So given a list of input molecules, AlphaFold3 generates their joint 3D structure, revealing how they all fit together. It models large biomolecules such as proteins, DNA and rna, as well as small molecules, also known as ligands, a category encompassing many different drugs. And they can model chemical modifications to these molecules, which control the healthy functioning of cells that when disrupted, can lead to the disease. We also see that AlphaFold3's capabilities come from its next generation architecture and training that now covers all of life's molecules. At the core of the model is an improved version of the evoforma module, which is essentially a module that kind of learns the grammar of protein folding by studying evolutionary examples and then uses that knowledge to predict the the 3D structure of new amino acid sequences, much like how we can kind of predict the meaning of a new sentence after learning the grammar of a language. And it's a deep learning architecture that underpinned Alpha Fold 2's incredible performance. And after processing the input, AlphaFold 3 assembles its predictions using a diffusion network akin to those found in AI image generators. And the diffusion process starts with a cloud of atoms and and over many steps, converges on its final, most accurate molecular structure. Now, here's where we have one of the predictions. So this in. Well, in this case, what we do have here is we have the ground truth shown in gray, and then we have the real one shown in the actual colors. So in this example, what we do have here is we have us looking at the spike of a protein of a common cold virus. The spike protein is a part of the virus that helps it infect our cells. And the AI model actually accurately predicted how this spike protein interacts with antibodies, which are the immune system's defense proteins that attach the virus and neutralize it. And of course, simple sugars. In this prediction that you're currently seeing on screen, the spike protein is in blue. The antibodies which try to stop the virus are shown in turquoise. So the simple sugars are also shown in yellow. The and these predictions essentially closely match what scientists have observed in real life experiments, which are shown in gray. By using animations of this interaction, scientists can then see exactly how the virus actually interacts with antibodies and sugars. And this information is crucial because understanding how these immune system processes helps us figure out how to fight different viruses, including COVID 19, leading to the potential of better treatments. And AlphaFold 3 saves so much time by providing accurate predictions that would otherwise require lengthy and expensive laboratory equipments. And it does this because determining the 3D structure of proteins using experimental methods like x ray crystallography or cryo electron microscopy can take months or even years. And AlphaFold3 can predict these structures in literally hours or days. And with predicted structures available, scientists can then focus on the most promising drug target or biological questions without spending time exploring dead ends. And this allows researchers to test hypotheses. So there's hypothesis generation where it can generate new hypotheses about how biological molecules function or interact. And researchers can then test these hypotheses directly, Reducing the need for broad exploratory studies. Now, there was also another example here in which we can see Tim 3. This is a protein that researchers are studying because it might be useful, a useful target for cancer treatments. And scientists found a way to create small molecules that could stick to this protein and potentially block the harmful effects. Now, before this study, there weren't any clear images showing how these small drug like molecules would actually fit into the Tim3's structure. And without this information, designing effective drugs would was pretty challenging. So essentially, Alpha Fold 3 comes in. Scientists use Alpha Fold 3 to predict what Tim 3 would look like when these small molecules bind to it. And they only provided the AI with the protein sequence, which is essentially the recipe and a simple description of the drug like molecules. So that's where we get to the outcome. So AlphaFold3 actually predicted how these molecules would fit together perfectly into the pocket of the Tim3 protein, aligning almost exactly with the structures that scientists discovered through experiments. It also showed that without these drug molecules present, the pocket actually looked different, proving that Alpha Fold 3 can actually recognize changes in protein shapes when other molecules are around. And this essentially means that Alpha Fold 3 can predict how drug molecules will interact with proteins more accurately than ever before. And this is going to lead to faster and more efficient drug discovery, Helping scientists develop better treatments for cancer and other diseases. And of course, it makes sense to talk about Isomorphic labs. So one of the things that they talk about leading this drug discovery at Isomorphic labs is that alpha fold 3 is 50% more accurate than the best traditional methods on the pose busters benchmark, without needing the input of any structural information, making AlphaFold 3 the first AI system to surpass physics based tools for biomolecular structure prediction. And Isomorphic Labs is using AlphaFold 3 to accelerate and improve the success of drug design by helping understand how to approach new disease targets and developing novel ways to pursue existing ones that were previously out of reach. They also talk about how we can now create and test hypotheses at the atomic level and produce highly accurate structure predictions within seconds and standing in stark contrast to the months or even years required to experimentally determine answers to similar questions. Now they also talk about how they're using this. They state that already we are using Alpha Fold 3 day to day. Our scientists have seen that designing small molecules against Alpha4 3's structural predictions helps create designs that bind effectively to a target protein. The improved structural accuracy of protein to protein interfaces with Alpha 43 opens up the possibility of designing new treatment modalities such as antibodies or other therapeutic proteins. And a richer understanding of a novel target can be achieved by looking at the structure of targets in their full biological context in complex with other protein binding partners, DNA, RNA and ligand cofactors. And of course there was a paper that was released with the AlphaFold 3 release. However, this is currently an accelerated article preview, an aap, which is a version of a scientific article that's published online before it appears in a traditional print journal. And it basically just means that it's just designed to get important research findings out to scientists and the public as quickly as possible. And they kind of skip some of the editing and formatting that can actually slow down a traditional journal publication. And that means that there is like a watermark on each page to remind you that they aren't final, the final polished version. Now here's where we actually talk about the main thing, which is of course the AlphaFold server. So Google actually launched this tool that actually helps you do this stuff. So scientists can actually use this for free. There's no fancy subscription needed and basically with a few clicks any biologist can use AlphaFold3 to make models of proteins, DNA, RNA and other important molecules. And this is pretty huge because at the AlphaFold server lets scientists quickly come up with new ideas to test out in the lab. And this cuts out the time on wasting guesswork. So just picture your science projects going way faster. And remember, this used to take you a huge amount of time and now all of that is being saved. So this is the quick trailer. I'll leave you guys with this and if you need any of the links, they'll be either at a top comment or a link in the description. Foreign.