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.'...

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein. This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data. Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications. 'Protein design is a hard problem because the mapping from DNA sequence to protein structure and function is really complex. There might be a great protein 10 changes away in the sequence, but each intermediate change might correspond to a totally nonfunctional protein. It's like trying to find your way to the river basin in a mountain range, when there are craggy peaks along the way that block your view. The current work tries to make the riverbed easier to find,' says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT's McGovern Institute for Brain Research, director of the K. Lisa Yang Integrative Computational Neuroscience Center, and one of the senior authors of the study....