But his most consequential work to date, perhaps, is the development of AlphaFold 2<\/a>\u2014an AI system, unveiled by DeepMind in 2020, that could successfully predict the three-dimensional structures of proteins from their DNA sequences. AlphaFold 2 was a generational scientific achievement with implications for better understanding and even curing diseases like Parkinson\u2019s, muscular dystrophy, and certain cancers, all of which stem from misfolded or malfunctioning proteins. It won Hassabis and DeepMind scientist John Jumper the 2024 Nobel Prize in Chemistry<\/a>; that same year, Hassabis was knighted.\u00a0<\/p>\n To Sir Demis, it\u2019s all connected. His early fascination with the skies has through lines to AI, finding order and meaning amid seeming randomness.\u00a0<\/p>\n \u201cThe night sky is a mystery that\u2019s staring us in the face all the time,\u201d he says. \u201cIt\u2019s a constant reminder of the bigger questions. I think that is how I got into vastness\u2026You\u2019ve got to find patterns in huge amounts of data, or find the right move in huge amounts of possibilities.\u201d<\/p>\n Hassabis, for the past few years, has been devoting an important share of his 100-hour workweek to one of the world\u2019s greatest pattern-recognition problems: drug discovery. In 2021, with funding from Google parent Alphabet, Hassabis started Isomorphic Labs<\/a>, an AI drug-design company that aims to create new, breakthrough medicines for some of the most \u201cundruggable\u201d diseases\u2014with the hyper-ambitious goal, as the startup\u2019s splashy tagline puts it, to \u201csolve all disease.\u201d<\/p>\n Isomorphic has been quiet since launch and has yet to move a drug to the make-or-break clinical trial phase. But recent moves suggest that milestone isn\u2019t far off<\/a>, and its backers argue that Isomorphic\u2019s approach will give it an edge once it enters the fray. The startup recently opened its doors to Fortune; I spent three days talking with its executives and scientists about what\u2019s arguably AI\u2019s biggest opportunity and challenge.\u00a0<\/p>\n \u201cWe\u2019re trying to build a system, a process\u2026to do maybe dozens of drugs each year.\u201dDemis Hassabis, Founder and CEO, Isomorphic Labs<\/p>\n \u201cA biotech startup might do one or two drugs its entire corporate life,\u201d says Hassabis. \u201cBut we\u2019re trying to build a system, a process, and all the technology to do maybe dozens of drugs each year. That seems crazy right now, but I think eventually, over the next 10 to 20 years, we could get to finding a solution to all disease\u2026if we have a process that can find these needles in a haystack.\u201d<\/p>\n Drug discovery is more like finding a needle in Iowa: It\u2019s a process of testing potentially therapeutic compounds against the infinite variables of biology, characterized by continual setbacks and an astronomical failure rate.\u00a0<\/p>\n Though it addressed only a small part of that process, AlphaFold offered hope for a break from that reality\u2014some of the first seismic proof that AI could take a brute-force, grind-it-out problem in medical science and compress a process that once took years into minutes. After that breakthrough, Hassabis founded Isomorphic with a simple idea: What if you could turn AlphaFold into a full-fledged drug-design engine?\u00a0<\/p>\n The resulting spinoff aims to succeed in a space where many have failed by focusing on structure: using AI to generate detailed molecular-level predictions about the interactions of drugs with their targets, thus stripping out much of the time-consuming trial and error that defines the pre-clinical-trial stages of drug discovery\u2014and elevating the brash notion of \u201csolving\u201d disease to the realm of the possible.\u00a0<\/p>\n After its spinoff, Isomorphic initially raised money from Alphabet, falling into the behemoth\u2019s \u201cOther Bets\u201d bucket. In March 2025, the company raised an additional $600 million<\/a>, in a Series A led by Joshua Kushner\u2019s Thrive Capital that included participation from Google Ventures, which was involved from the inception. (Isomorphic declined to disclose the valuation.) The bet: that over time, we will design drugs that cure previously intractable diseases like cancer and Alzheimer\u2019s, with new techdriven processes so precise they seem almost magical right now\u2014but that, eventually, will become standard.\u00a0<\/p>\n \u201cNo one would visualize designing an airplane today by hand, nor would you want to fly an airplane designed by hand,\u201d says Thrive Capital partner Vince Hankes. \u201cBut all of our drugs are designed like that. In the future, they should all be designed with robust software and intelligence and simulation, just like we design airplanes today.\u201d\u00a0<\/p>\n Isomorphic\u2019s 300 or so employees are aiming to do just that, with Hassabis as, so to speak, their pilot.<\/p>\n Brutally long odds<\/p>\n There are far more possible chemical compounds than stars in the observable universe\u2014about 10^60, or 10 to the 60th power, according to the latest research. That estimate covers only small, drug-like molecules and may ultimately be low. Figuring out which of those combinations might tame a tumor or dangerous mutation is the task Hassabis and his peers hope to solve with AI.\u00a0<\/p>\n Throughout most of history, there were very few drugs, and many of the ones that did exist were discovered by accident. (Penicillin, discovered as a result of an accidental mold contamination, is the most famous example.) In the 1960s, drug discovery picked up, as early cancer and cardiovascular treatments emerged. But for much of the 20th century, scientists scoured the chemical universe with a combination of brute force and slowly improving technology. Many chemists spent their careers boiling sludge, running lab tests, and starting from scratch\u2014and usually failing. Even today, according to widely cited industry figures, only one in 20 drug discovery chemists will successfully bring a drug to market during their careers.\u00a0<\/p>\n \u201cThere are lots of different parameters you\u2019re trying to triangulate into one molecule that\u2019s a perfect match for a specific problem,\u201d explains Miles Congreve, chief scientific officer at Isomorphic. \u201cYou might find that you\u2019ve got a great target, it\u2019s a potent compound, and it does very well. But there are other things that aren\u2019t right\u2014and you go down a dead end. It\u2019s a bit like Whac-a-Mole.\u201d<\/p>\n Congreve is anomalous among medicinal chemists: He has helped get three cancer drugs to market, including Novartis<\/a> and Astex Pharmaceuticals\u2019 ribociclib, which treats breast cancer. Industrywide, even getting a drug to clinical trials is often considered a massive win. But as he points out, \u201cHistorically, there\u2019s at least a 90% failure rate\u201d on such trials. \u201cYour chances of finding that perfect molecule are infinitesimally small,\u201d agrees Fiona Marshall, president of biomedical research at Novartis.<\/p>\n Those odds help explain just how shocked a globeful of scientists was that AlphaFold 2 worked so well. That breakthrough, in turn, has helped Isomorphic attract talent. Melissa Davis, director of computational biology, says she came aboard precisely because she was intrigued by building on AlphaFold. \u201cPeople would spend their whole career trying to crystallize one membrane protein,\u201d notes Davis. \u201cAll of a sudden, you didn\u2019t have to spend five or six years trying to get a structure for the protein anymore. Any scientist could generate one like that.\u201d<\/p>\n Other top staff have longer histories with Hassabis. Max Jaderberg\u2014who served as Isomorphic\u2019s chief AI officer for four years before being named in November to succeed longtime Hassabis collaborator Colin Murdoch as president\u2014spent seven years at DeepMind developing (among other things) AlphaStar, the first AI to best a human professional at the video game StarCraft II. Jaderberg is prominent among a cohort of DeepMinders who followed Hassabis to Isomorphic (they make up about 11% of the company\u2019s staff).\u00a0<\/p>\n \u201cIt\u2019s humbling when rubber meets the road, with real wet-lab work.\u201d<\/p>\n Max Jaderberg, President, Isomorphic Labs<\/p>\n \u201cIt\u2019s always humbling to hear you have medicinal chemists who will do their whole career without creating a single successful drug,\u201d says Jaderberg. \u201cContrast that with someone like myself, who comes from the AI world where you have to smash the best in the world every six months or you\u2019re dead.\u201d He adds, \u201cIt\u2019s humbling when rubber meets the road, with real scientific processes and real wetlab work.\u201d<\/p>\n Getting the right talent is one of Hassabis\u2019s priorities, given that his crammed schedule limits his time at Isomorphic: He\u2019s at the startup office one day a week, usually a Tuesday, when he meets with its executive team and sets priorities for the company\u2019s technical direction.\u00a0<\/p>\n Hassabis jokes that he loves managing \u201chigh-maintenance geniuses,\u201d and that he\u2019s looking for those with a creative streak. \u201cAny professional scientist will already be very good technically,\u201d says Hassabis. \u201cBut then can you come up with a creative new idea, or ask the right question? That\u2019s actually harder. Finding the answer is actually finding the right question.\u201d<\/p>\n Structure first<\/p>\n What Isomorphic calls its structure-first approach is, Jaderberg explains, a choice of generalization over specialization. The startup is focusing on an effort to map more and more of the complex biological constellations of the body, the better to predict how any compound might affect a range of diseases and other biological processes. CTO Sergei Yakneen says it\u2019s all about working toward a precision that once would have seemed unfathomable, like landing a rocket on the side of the moon you can\u2019t see.\u00a0<\/p>\n Its core technology is a drug-design engine built around a number of proprietary models. The engine incorporates an updated protein-predicting model, plus models for peptides, molecular glues, and antibodies. The data the engine is built on includes a combination of the global Protein Data Bank, the U.K. Biobank, commercially licensed sources, internally generated datasets, and data from partners.\u00a0<\/p>\n BARRY CRASKE\/COURTESY OF ISOMORPHIC<\/p>\n The task is partially squeezing more insight out of existing data\u2014 something others have tried to do in the past, often without success, Yakneen acknowledges. \u201cThen, lo and behold,\u201d he adds, \u201cwith the right skills, you\u2019re able to build these mind-blowing systems.\u201d\u00a0<\/p>\n Isomorphic won\u2019t say what diseases it\u2019s targeting in the short term\u2014a secrecy that\u2019s normal in pharma and slightly odd in tech. The company points to its partnerships<\/a> with pharma giants Eli Lilly<\/a> and Novartis as evidence of its progress. (The Novartis partnership was expanded in 2025<\/a>.)<\/p>\n In conversation, however, multiple executives say they\u2019re focused on drugging the undruggable. This is a widely used phrase in drug development that means something relatively specific: tackling protein mutations that are particularly prevalent in pancreatic, lung, and colorectal cancers, along with transcription factors, which are widespread across various cancer types. All of these cancers have hitherto been resistant to treatment, but they\u2019re likely the kinds of codes Isomorphic is most committed to cracking.<\/p>\n Saving five years, or more<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/ie\/wp-content\/uploads\/2026\/01\/2025_11_25_Isomorphic_Labs_02_Max_Jaderberg_Editorial_0244.jpg\"\/)
Before tackling drug development, Max Jaderberg worked on DeepMind AI that mastered the video game StarCraft II.<\/p>\n