Google DeepMind Open-Sources AlphaFold 3: A Game-Changer for Drug Discovery and Molecular Biology

Google DeepMind's New AI Model Can Help in Drug Discovery

DeepMind believes the AI model can lead to new drug discovery Google DeepMind has silently open-sourced its frontier artificial intelligence (AI) model that can predict the interaction between proteins and other molecules. Dubbed AlphaFold 3, the large language model is the successor of AlphaFold 2, whose research led to the creators of the large language model (LLM) Demis Hassabis and John Jumper getting the Nobel Prize in Chemistry in 2024. AlphaFold 3 takes the capabilities further with its ability to model proteins' interaction with DNA, RNA, and other smaller molecules which can potentially lead to drug discovery. Research on protein structures has been one of the major areas of focus in Chemistry. Since the 3D shape and atomic details of proteins are the targets for drugs, discovering new protein structures can often open previously unexplored targets and mechanisms for medical intervention. Put simply, the better we understand protein structures, the more effective medicines can be against various disorders, diseases, and autoimmune disorders. While Google DeepMind made no announcement about releasing the AlphaFold 3 AI model, it has made the source code and model weights available on GitHub. However, this is only available for academic and research purposes. The source code is available freely under a Creative Commons licence, however, the weights can only be accessed after obtaining direct permission from Google for academic use. It is believed that if the AI model can correctly highlight how proteins interact with DNA, RNA, and other smaller molecules, researchers will be able to accelerate the manufacturing of new synthetic drugs. Researchers will also be able to automate work that could have taken them years without any proof of success. AlphaFold 3 comes three years after the release of AlphaFold 2 in 2021. In a study, the lead author highlighted that drug discovery could become much easier with the help of the AI model. The AlphaFold 3 is trained on a vast amount of research material and datasets about protein structures and their interaction with other molecules. By understanding the context and logic of protein structures, the LLM can predict how certain target zones will react when they come in contact with certain molecules.

Google DeepMind open-sources AlphaFold 3, ushering in a new era for drug discovery and molecular biology

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google DeepMind has unexpectedly released the source code and model weights of AlphaFold 3 for academic use, marking a significant advance that could accelerate scientific discovery and drug development. The surprise announcement comes just weeks after the system's creators, Demis Hassabis and John Jumper, were awarded the 2024 Nobel Prize in Chemistry for their work on protein structure prediction. AlphaFold 3 represents a quantum leap beyond its predecessors. While AlphaFold 2 could predict protein structures, version 3 can model the complex interactions between proteins, DNA, RNA, and small molecules -- the fundamental processes of life. This matters because understanding these molecular interactions drives modern drug discovery and disease treatment. Traditional methods of studying these interactions often require months of laboratory work and millions in research funding -- with no guarantee of success. The system's ability to predict how proteins interact with DNA, RNA, and small molecules transforms it from a specialized tool into a comprehensive solution for studying molecular biology. This broader capability opens new paths for understanding cellular processes, from gene regulation to drug metabolism, at a scale previously out of reach. The timing of the release highlights an important tension in modern scientific research. When AlphaFold 3 debuted in May, DeepMind's decision to withhold the code while offering limited access through a web interface drew criticism from researchers. The controversy exposed a key challenge in AI research: how to balance open science with commercial interests, particularly as companies like DeepMind's sister organization Isomorphic Labs work to develop new drugs using these advances. The open-source release offers a middle path. While the code is freely available under a Creative Commons license, access to the crucial model weights requires Google's explicit permission for academic use. This approach attempts to satisfy both scientific and commercial needs -- though some researchers argue it should go further. The technical advances in AlphaFold 3 set it apart. The system's diffusion-based approach, which works directly with atomic coordinates, represents a fundamental shift in molecular modeling. Unlike previous versions that needed special handling for different molecule types, AlphaFold 3's framework aligns with the basic physics of molecular interactions. This makes the system both more efficient and more reliable when studying new types of molecular interactions. Notably, AlphaFold 3's accuracy in predicting protein-ligand interactions exceeds traditional physics-based methods, even without structural input information. This marks an important shift in computational biology: AI methods now outperform our best physics-based models in understanding how molecules interact. The impact on drug discovery and development will be substantial. While commercial restrictions currently limit pharmaceutical applications, the academic research enabled by this release will advance our understanding of disease mechanisms and drug interactions. The system's improved accuracy in predicting antibody-antigen interactions could accelerate therapeutic antibody development, an increasingly important area in pharmaceutical research. Of course, challenges remain. The system sometimes produces incorrect structures in disordered regions and can only predict static structures rather than molecular motion. These limitations show that while AI tools like AlphaFold 3 advance the field, they work best alongside traditional experimental methods. The release of AlphaFold 3 represents an important step forward in AI-powered science. Its impact will extend beyond drug discovery and molecular biology. As researchers apply this tool to various challenges -- from designing enzymes to developing resilient crops -- we'll see new applications in computational biology. The true test of AlphaFold 3 lies ahead in its practical impact on scientific discovery and human health. As researchers worldwide begin using this powerful tool, we may see faster progress in understanding and treating disease than ever before.

AlphaFold 3 is Now Open Source -- A New Era in Protein Prediction

Google DeepMind has finally open sourced the AlphaFold 3 model, making its training weights accessible to academic researchers and scientists -- for non-commercial use only. Check out the model here. "The AlphaFold 3 model code and weights are now available for academic use. We at Google DeepMind are excited to see how the research community continues to use AlphaFold to address open questions in biology and new lines of research," said Google DeepMind's Pushmeet Kohli, as promised six months ago with the announcement to expand AlphaFold 3's accessibility for the scientific community. In May 2024, Google DeepMind released AlphaFold 3, a game changing protein folding model that predicts with 50% better accuracy. It is capable of predicting the structure and interactions of all biological molecules, including proteins, DNA, RNA, and ligands. Founded by nobel prize winner Demis Hassabis in 2021, Isomorphic Labs -- a sister company of Google DeepMind -- is looking to revolutionise drug discovery with AI, potentially building a multi-$100 billion business by accelerating research and improving clinical trial success. "AI-designed drugs would probably be available in the next couple of years," he said. "I hope to achieve both (commercial success and societal benefits) with Isomorphic and build a multi-100 billion dollar business. I think it has that potential," said Hassabis without delving into the specific timeline. The brains behind Isomorphic also include tech veteran Miles Congreve, serving as chief scientific officer, who contributed to the design of 20 clinical-stage drugs and co-invented Kisqali (Ribociclib), a marketed breast cancer treatment. Also noteworthy is Sergei Yakneen's contribution, who is the chief technology officer with over two decades of expertise in engineering, machine learning, product development, and life sciences and medicine research. The company recently announced key partnerships with two of the world's largest pharmaceutical companies -- Eli Lilly & Co. and Novartis AG. The deals are said to have a combined value of close to $3 billion. Two months ago, Google DeepMind, in collaboration with Isomorphic Labs, predicted over 200 million protein structures using AlphaFold. It achieved this by training the model with nearly 100,000 known proteins, driving significant breakthroughs in drug discovery by targeting previously intractable biomedical challenges. The model can predict the 3D structure of proteins with incredible accuracy, aiming to design drugs that target specific proteins, unlocking treatments for diseases that were previously untreatable. Recently, Google DeepMind also launched AlphaProteo, an AI system that generates novel proteins designed to bind to specific target molecules poised to significantly advance research in drug design, disease understanding and other health applications.

AI protein-prediction tool AlphaFold3 is now open source

AlphaFold3 is open at last. Six months after Google DeepMind controversially withheld code from a paper describing the protein-structure prediction model, scientists can now download the software code and use the artificial intelligence (AI) tool for non-commercial applications, the London-based company announced on 11 November. "We're very excited to see what people do with this," says John Jumper, who leads the AlphaFold team at DeepMind and last month, along with CEO Demis Hassabis, won a share of the 2024 Chemistry Nobel Prize for their work on the AI tool. AlphaFold3, unlike its predecessors, is capable of modelling proteins in concert with other molecules. But instead of releasing its underlying code -- as was done with AlphaFold2 -- DeepMind provided access via a web server that restricted the number and types of predictions scientists could make. Crucially, the AlphaFold3 server prevented scientists from predicting how proteins behave in the presence of potential drugs. But now, DeepMind's decision to release the code means academic scientists can predict such interactions by running the model themselves. The company initially said that making AlphaFold3 available only through a web server struck the right balance between enabling access for research and protecting commercial ambitions. Isomorphic Labs, a DeepMind spinoff company in London, is applying AlphaFold3 to drug discovery. But the publication of AlphaFold3 without its code or model weights -- parameters obtained by training the software on protein structures and other data -- drew criticism from scientists, who said the move undermined reproducibility. DeepMind swiftly reversed course and said it would make an open-source version of the tool available within half a year. Anyone can now download the AlphaFold3 software code and use it non-commercially. But for now, only scientists with an academic affiliation can access the training weights on request. DeepMind has got competition: over the past few months, several companies have unveiled open-source protein structure prediction tools based on AlphaFold3, relying on specifications described in the original paper known as pseudocode. Two Chinese companies -- technology giant Baidu and TikTok developer ByteDance -- have rolled out their own AlphaFold3 inspired models, as has a start-up in San Francisco, California, called Chai Discovery. A key limitation of these models is that, like AlphaFold3, none is licensed for commercial applications such as drug discovery, says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City. However, Chai Discovery's model, Chai-1, can be used via a web server for such work, says Jack Dent, a co-founder of the company. Another firm, San Francisco-based Ligo Biosciences, has released a restriction-free version of AlphaFold3. But it doesn't yet have the full suite of capabilities, including the capacity to model drugs and molecules other than proteins. Other teams are working on versions of AlphaFold3 that don't come with such limits: AlQuraishi hopes to have a fully open-source model called OpenFold3 available by the end of the year. This would enable drug companies to retrain their own versions of the model using proprietary data, such as the structures of proteins bound to different drugs, potentially improving performance. The last year has seen a flood of new biological AI models released by companies with varying approaches to openness. Anthony Gitter, a computational biologist at the University of Wisconsin-Madison, has no problem with for-profit companies joining his field -- so long as they play by the same rules as other scientists when they share their work in journals and preprint servers. If DeepMind makes claims about AlphaFold3 in a scientific publication, "I and others expect them to also share information about how predictions were made and put the AI models and code out in a way that we can inspect," Gitter adds. "My group's not going to build on and use the tools that we can't inspect." The fact that several AlphaFold3 replications have already emerged shows that model was reproducible, even without open-source code, says Pushmeet Kohli, DeepMind's head of AI for science. He adds that in future he would like to see more discussion about the publishing norms in a field increasingly populated by both academic and corporate researchers. The open-source nature of AlphaFold2 led to a flood of innovation from other scientists. For instance, the winners of a recent protein design contest used the AI tool to design new proteins capable of binding a cancer target. Jumper's favourite recent AlphaFold2 hack was from a team that used the tool to identify a key protein that helps sperm attach to egg cells. Jumper can't wait for such surprises to emerge after sharing AlphaFold3 -- even if they don't always bear fruit. "People will use it in weird ways," he predicts. "Sometimes it will fail and sometimes it will succeed."

Google DeepMind releases code behind its most advanced protein prediction program

Six months after backlash, AI company fulfills pledge to make AlphaFold3's full computer model available for noncommercial use Better late than never: Google DeepMind has today released the computer code underlying its latest AI protein prediction software to an eager research community. Many scientists are pleased by the move, though some remain upset it took 6 months for the company to reach this point. When DeepMind announced AlphaFold3 in a Nature paper on May 8, researchers lauded the technology's ability to predict not only proteins' structures, but also how they interact with DNA, RNA, and other proteins, a boon for drug discovery and other fields. But they criticized the announcement itself: Despite Nature's editorial guidelines stating computational code must be made available alongside published studies, the new paper contained only 'pseudocode' -- a list of steps a program runs -- and a link to an online portal that allowed scientists to do a limited number of predictions daily. The approach contrasted with DeepMind's publication of AlphaFold2, complete with code, in Nature in 2021, and ran counter to accepted standards of openness, reproducibility, and peer-review, researchers argued in an open letter that garnered hundreds of signatures. Following the backlash, DeepMind committed to releasing the full code for noncommercial use within six months of the paper's publication. Now, it has made good on that promise. The computational model itself was made public today on the code repository GitHub with a noncommercial license, while the 'weights' -- numbers that help tune how an AI model works -- are available to academics who complete a short application form. "We want to thank the community for the patience," says Pushmeet Kohli, vice president of science at DeepMind. Although he and his colleagues stand by how they released the program, Kohli says, they recognized the community's desire to work with the code directly. It has taken months to prep and test the model for today's public release, he adds. Researchers applaud the move. "I'm delighted that [the DeepMind team] is keeping their promise to release the code, because this means the actual in-depth review of an important paper can finally start," says Erik Lindahl, a biophysicist at Stockholm University and signatory on the open letter. "The model and weights being released are of huge importance" for efforts to evaluate and build on the work, adds Stephanie Wankowicz, a computational structural biologist at Vanderbilt University and an organizer of the letter. Still, she says, "the delay of six months is unacceptable." AlphaFold3 is the latest incarnation of AlphaFold, the AI that revolutionized the prediction of protein structures based solely on their amino acid sequence and won two DeepMind researchers, John Jumper and Demis Hassabis, a share of the Nobel Prize in Chemistry earlier this year. Until today, however, researchers could only use the program only via DeepMind's online portal, which permitted just 10 (and now 20) requests per day with a restricted set of molecules. In a statement in May, Nature's editor-in-chief Magdalena Skipper did not specify why the journal had waived its requirement to share the full code but said editors considered factors like "potential implications for biosecurity and the ethical challenges this presents." A news story in Nature, meanwhile, quoted Kohli as suggesting the team had restricted access to AlphaFold3 to avoid compromising the ability of Isomorphic Labs, a commercial spinoff from DeepMind, to pursue drug discovery plans. Kohli now tells Science that DeepMind's team prioritized developing the portal rather than releasing code "to make sure that we provided the easiest interface to the most ... people." Jumper says researchers have done some "incredible work" via the portal, which remains unchanged by today's news, and says he suspects most scientists will continue to work this way, as it's more practical for groups with limited computing power. The DeepMind researchers also contend that, contrary to some critics' claims, the Nature paper was reproducible, as demonstrated by the fact that multiple groups have since made their own versions of AlphaFold3 based on the pseudocode. AI-focused companies such as Baidu, Ligo Biosciences, and Chai Discovery have already released the results of such efforts. These alternative "implementations" will likely still be useful, even with AlphaFold3's code now released, notes Daniel Buchan, a bioinformatics researcher at University College London. For one thing, "it's good and important that methods can be replicated," he says. Comparing and contrasting the models will likely lead to improvements in the future, Wankowicz adds. Particularly important are implementations that are free from restrictive user licenses, like the one being developed by the nonprofit OpenFold consortium, researchers say. Otherwise, "if I help a colleague ... with a novel ligand that might be a good lead cancer drug, and at some point they want to work with a pharmaceutical company to commercialize it, things can get very complicated," says Roland Dunbrack, a computational structural biologist at the Fox Chase Cancer Center who was inititially asked to review DeepMind's manuscript for Nature but never received the code to do so. A number of research teams already have plans to work with AlphaFold3's code. The team behind a paper out today in Nature Computational Science, which describes a program called MassiveFold, says it wants to integrate AlphaFold3 into its software. MassiveFold helps users take advantage of parallel computing to reduce the time it takes to run lots of predictions in AlphaFold2 -- potentially from months to hours. By integrating DeepMind's new code, "the user will be able to get the best predictions [with this approach] from either AlphaFold2 or AlphaFold3," says MassiveFold developer Guillaume Brysbaert, a research engineer in bioinformatics at CNRS in France. Jumper says the DeepMind team is looking forward to what today's public release brings. "In AlphaFold2, we saw so much creativity," he says. "I'm really excited to see what the ... community discovers about how AlphaFold3 works -- how can it be applied to new problems?"