Proteins are the workhorses that keep our cells running, and there are many thousands of types of proteins in our cells, each performing a specialized function. Researchers have long known that the structure of a protein determines what it can do. More recently, researchers are coming to appreciate that a protein's localization is also critical for its function. Cells are full of compartments that help to organize their many denizens. Along with the well-known organelles that adorn the pages of biology textbooks, these spaces also include a variety of dynamic, membrane-less compartments that concentrate certain molecules together to perform shared functions. Knowing where a given protein localizes, and who it co-localizes with, can therefore be useful for better understanding that protein and its role in the healthy or diseased cell, but researchers have lacked a systematic way to predict this information. Meanwhile, protein structure has been studied for over half-a-century, culminating in the artificial intelligence tool AlphaFold, which can predict protein structure from a protein's amino acid code, the linear string of building blocks within it that folds to create its structure. AlphaFold and models like it have become widely used tools in research....

First, Hopfield and Hinton, two of the precursors of neural nets, the data-compressing algorithm that underpins developments like ChatGPT, were awarded the Nobel Prize in Physics and, yesterday, three other computer scientists/neuroscientists/chemists, two of which work at Google Deepmind (the CEO, Demis, and John, director), were awarded the Nobel Chemistry Prize, for their contributions to protein folding prediction. As for the Physics prize, the consensus is that both Hopfield and Hinton have been instrumental to the success of Deep Learning, the AI field that comprises neural networks and explains much of the progress of the field over the last two decades. However, their election has been met with mixed feelings, especially from the very own AI field. Especially harsh was Jurgen Schmidhuber, one of the most prominent AI researchers, who directly accused the Academy of 'rewarding plagiarism' as, according to him, both rewardees failed to cite crucial developments by other researchers like Shun-Ichi Amari in their respective research....

In 2021, AI research lab DeepMind announced the development of its first digital biology neural network, AlphaFold. The model was capable of accurately predicting the 3D structure of proteins, which determines the functions that these molecules play. 'We're just floating bags of water moving around,' says Pushmeet Kohli, VP of research at DeepMind. 'What makes us special are proteins, the building blocks of life. How they interact with each other is what makes the magic of life happen.' AlphaFold was considered by the journal Science as the breakthrough of the year in 2021. In 2022, it was the most cited research paper in AI. 'People have been on [protein structures] for many decades and were not able to make that much progress,' Kohli says. 'Then came AI.' DeepMind also released the AlphaFold Protein Structure Database'which contained the protein structures of almost every organism whose genome has been sequenced'making it freely available to scientists worldwide. More than 1.7 million researchers in 190 countries have used it for research ranging from the design of plastic-eating enzymes to the development of more effective malaria vaccines. A quarter of the research involving AlphaFold was dedicated to the understanding of cancer, Covid-19, and neurodegenerative diseases like Parkinson's and Alzheimer's. Last year, DeepMind released its next generation of AlphaFold, which extended its structure prediction algorithm to biomolecules like nucleic acids and ligands....

Google spent much of the past year hustling to build its Gemini chatbot to counter ChatGPT, pitching it as a multifunctional AI assistant that can help with work tasks or the digital chores of personal life. More quietly, the company has been working to enhance a more specialized artificial intelligence tool that is already a must-have for some scientists. AlphaFold, software developed by Google's DeepMind AI unit to predict the 3D structure of proteins, has received a significant upgrade. It can now model other molecules of biological importance, including DNA, and the interactions between antibodies produced by the immune system and the molecules of disease organisms. DeepMind added those new capabilities to AlphaFold 3 in part through borrowing techniques from AI image generators. 'This is a big advance for us,' Demis Hassabis, CEO of Google DeepMind, told WIRED ahead of Wednesday's publication of a paper on AlphaFold 3 in the science journal Nature. 'This is exactly what you need for drug discovery: You need to see how a small molecule is going to bind to a drug, how strongly, and also what else it might bind to.'...