Google DeepMind CEO Predicts AI-Designed Drugs in Clinical Trials by 2025

Isomorphic Labs to Begin Trials for AI-Designed Drug by Year-End: Report

Project Astra, an AI assistant prototype, expected to roll out in 2025. Isomorphic Labs, a drug discovery startup owned by Google's parent Alphabet, will have its first AI-designed drug enter clinical trials by the end of 2025, according to founder Demis Hassabis. Speaking at the World Economic Forum, Hassabis revealed that the company is targeting major disease areas, including oncology, cardiovascular, and neurodegeneration, as reported by the Financial Times. Also Read: Novo Nordisk and Valo Health Expand AI Partnership for Drug Discovery The company, which was spun out of Google DeepMind in 2021, aims to reduce the drug development timeline -- traditionally 5 to 10 years -- by up to tenfold using artificial intelligence. Isomorphic Labs is collaborating with pharmaceutical companies Eli Lilly and Novartis on six drug development programs, according to the report. Hassabis, who also serves as chief executive of Google DeepMind, reportedly said the company's prototype AI assistant, known as Project Astra, is expected to roll out to consumers later this year. According to the report, he described a near future, within three years, where there are "billions" of AI agents, "negotiating with each other on behalf of the vendor and the customer." This development, he said, would require a rethinking of the web itself. However, Hassabis urged more caution and coordination among leading AI developers in developing artificial general intelligence (AGI), warning of potential misuse by malicious actors. He reportedly cautioned that the technology could threaten human civilisation if it runs out of control or is repurposed by "bad actors...for harmful ends." Google DeepMind's ultimate goal is to create artificial general intelligence, or "a system that is capable of exhibiting all the cognitive capabilities that humans have," according to Hassabis, who reportedly added that, despite social media "hype" about it being close, true AGI was still five to 10 years away. Also Read: Google Pushes AI Education to Change Global Narrative: Report "If something's possible and valuable to do, people will do it," Hassabis said, according to the report. "We're past that point now with AI, the genie can't be put back in the bottle...so we have to try and make sure to steward that into the world in as safe a way as possible."

Google-backed AI-developed drugs are headed to trial by 2026, says DeepMind CEO

Drugs developed by Alphabet's drug discovery subsidiary and designed by artificial intelligence are expected to head to trial by the end of the year, according to a Google (GOOGL) executive. "AI applied to science is a lot richer than just the language models," Google DeepMind CEO Demis Hassabis and the founder of Isomorphic Labs during a panel at the World Economic Forum in Davos, Switzerland, on Tuesday. "We'll hopefully have some AI-designed drugs in the clinic by the end of the year. That's the plan," he added. The four-year-old Isomorphic Labs was spun off from DeepMind in 2021 as a stand-alone subsidiary under Alphabet. In July, it announced deals to work on research with Eli Lilly & Co. (LLY) and Novartis (NVS) to leverage its AI technology -- namely AlphaFold, its model that predicts a protein's 3D structure -- to discover therapeutics against multiple targets. "We're looking at oncology, cardiovascular, neurodegeneration, all the big disease areas, and I think by the end of this year, we'll have our first drug," Hassabis told The Financial Times on Tuesday. But Isomorphic Labs isn't the only company working on AI-designed drugs. Interest in leveraging AI to help quickly discover new treatments has been booming. "There's a huge public health need to develop new antibiotics quickly," Stanford University Professor James Zou, who used generative AI to help creature structures and chemical recipes for six drugs, said last year. "Our hypothesis was that there are a lot of potential molecules out there that could be effective drugs, but we haven't made or tested them yet," he added. "That's why we wanted to use AI to design entirely new molecules that have never been seen in nature." Insilico Medicine, a Hong Kong-based startup with offices in New York and Boston, became the first company to send an AI-designed drug to clinical trials in 2023. The drug, INS018_055, was designed to treat idiopathic pulmonary fibrosis, the most common type of pulmonary fibrosis, a disease that causes scarring on the lungs. There's currently no cure for the disease, according to the United Kingdom's National Health Service. Trials are still ongoing, but Insilico in November said topline results from its study showed positive results. Earlier this month, Insilico released positive topline results from two phase 1 trials of an AI-designed drug meant to help people with Inflammatory Bowel Disease. The startup also said researchers had used its AI-driven platform to identify potential treatments for endometriosis.

AI-developed drug will be in trials by year-end, says Google's Hassabis

Isomorphic Labs, the four-year old drug discovery start-up owned by Google parent Alphabet, will have an artificial intelligence-designed drug in trials by the end of this year, says its founder Sir Demis Hassabis. "We're looking at oncology, cardiovascular, neurodegeneration, all the big disease areas, and I think by the end of this year, we'll have our first drug," he said in an interview with the Financial Times at the World Economic Forum. "It usually takes an average of five to 10 years [to discover] one drug. And maybe we could accelerate that 10 times, which would be an incredible revolution in human health," said Hassabis, who received the Nobel Prize for chemistry with his colleague John Jumper and biochemist David Baker in October. Isomorphic was spun out of Google's AI research arm Google DeepMind in 2021, but remains a wholly owned subsidiary of its parent company, Alphabet. The start-up's potential has attracted big pharmaceutical partners, which are keen to lower expenses and boost efficiency of the costly drug development process. Hassabis previously told the FT his team was working on six drug development programmes with Eli Lilly and Novartis. In a wide-ranging interview, Hassabis, who is also chief executive of Google DeepMind, said the search giant's prototype of an AI assistant, known as Project Astra, will probably roll out to consumers later this year. He described a near future, within three years, when there are "billions" of AI agents, "negotiating with each other on behalf of the vendor and the customer" and said it would require a rethinking of the web itself. He also called for more caution and co-ordination among leading AI developers competing to build artificial general intelligence. He warned the technology could threaten human civilisation if it runs out of control or is repurposed by "bad actors . . . for harmful ends". Google DeepMind's ultimate goal is to create artificial general intelligence, or "a system that is capable of exhibiting all the cognitive capabilities that humans have", according to Hassabis, who said that despite social media "hype" about it being close, true AGI was still five to 10 years away. "If something's possible and valuable to do, people will do it," Hassabis said. "We're past that point now with AI, the genie can't be put back in the bottle . . . so we have to try and make sure to steward that into the world in as safe a way as possible."

DeepMind CEO expects we'll pop AI-generated pills in 2025

Nobel Prize winner Demis Hassabis thinks human trials will happen soon Clinical trials of the first drugs designed with the help of artificial intelligence could commence this year, Google DeepMind CEO Demis Hassabis suggested Tuesday. Speaking on a panel at the World Economic Forum in Davos, Hassabis, who also runs DeepMind drug-discovery spin-off Isomorphic Labs, said he expected to have "some AI-designed drugs in clinical trials by the end of the year... That's the plan." Isomorphic Labs has tried to speed up the development of medicines using machine learning since 2021. "Eventually you could imagine personalized medicine where it's optimized, maybe overnight, by an AI system for your personal metabolism," he said. AI hype is currently omnipresent, though Hassabis and his colleague John Jumper earned a Nobel Prize for work AlphaFold, a deep learning system that can predict protein structures. Pharmaceutical companies are interested in AI because it has the potential to save them lots of time and money. According to a recent article published in the Journal Nature Medicine, successfully creating a new drug and having it approved for use can take 12 to 15 years and costs roughly $2.6 billion. Many drugs are never approved for use, as fewer than ten percent of clinical trials in which humans consume the drug succeed. Anything that can reduce costs, speed development, or increase success rates will make a material impact on pharma companies' bottom lines. Researchers believe there are many ways in which machine learning models can improve and speed parts of the drug discovery process. Hassabis believes huge savings in time and cost could be possible. Optimism of that sort needs to be tempered because high-quality training data is hard to come by, due to privacy regulations, data-sharing policies, and acquisition costs. Hassabis believes those challenges aren't insurmountable. "You can generate some key data to fill in the gaps of where the public data doesn't have it," he said. This can be done in collaboration with clinical research organizations or through the use of synthetic data, something he said AlphaFold2 used extensively. However, as we've previously discussed, synthetic data can be problematic. "You've got to be very careful if you're using synthetic data, that it's actually correctly representing the distribution and you're not somehow training on your own errors," Hassabis said. Hassabis doesn't think AI will replace scientists anytime soon. "True invention is not possible yet with AI. It can't come up with a new hypothesis or new conjecture. It can maybe solve a complicated conjecture in, say, maths. I think we're very close to some big breakthroughs on that front. I think we'll actually see that this year, but that's different from actually coming up with the theory or the hypothesis, as the best human scientists do," he said. Hassabis is not alone in exploring the application of machine learning on drug discovery. Nvidia has also shown enthusiasm for AI-augmented drug discovery, perhaps because it will create more reasons to buy its hardware. Last northern autumn, Nvidia open sourced its BioNeMo family of GPU-accelerated machine learning frameworks for drug development and molecular design. The company has also taken steps to repackage existing models like DeepMind's AlphaFold2 and MIT's DiffDock 2.0 as microservices to make them easier to consume. Nvidia is also partnering with major pharma companies, including Danish pharmaceutical giant Novo Nordisk, to bring new research systems online. Denmark's Gefion supercomputer, which applies apply machine learning to biological sciences and the development of new treatments, is one example of such efforts. ®

Google DeepMind CEO: AI-Designed Drugs Coming to Clinical Trials in 2025 | PYMNTS.com

Nobel laureate and Google DeepMind CEO Demis Hassabis said Tuesday (Jan. 21) that he expects to see pharmaceutical drugs designed by artificial intelligence (AI) to be in clinical trials by the end of the year. During a fireside chat at the World Economic Forum in Davos, Switzerland, Hassabis said these drugs are being developed at Isomorphic Labs, a for-profit venture created by Google parent firm Alphabet in 2021 that was tasked to reinvent the entire drug discovery process based on first principles and led by AI. "That's the plan," Hassabis said. While large language models have taken the spotlight, Hassabis said that the field of AI as it applies to science is "a lot richer than just the language models and things like AlphaFold." AlphaFold2, for which he and colleague John Jumper won the Nobel Prize in Chemistry in 2024, is an AI model that predicts the 3D structure of proteins, solving a half-century biology challenge. Ardem Patapoutian, a professor of neuroscience at Scripps Research and a 2021 Nobel laureate in Physiology or Medicine who joined Hassabis at the panel, described AlphaFold as "one of the most amazing, quick advancements in science I've ever experienced." He said 25 years ago, it took a Ph.D. student five years to find out the structure of a protein. With AlphaFold, "just type in the sequence and it tells you the structure," he said. Hassabis said AlphaFold has now predicted the structures of 200 million proteins known to science -- work that would have taken an estimated billion years using traditional methods. The latest version, AlphaFold3, has expanded capabilities to analyze protein interactions with other proteins, ligands and DNA/RNA. Patapoutian also highlighted AI's potential to unlock the mysteries of brain function, particularly in understanding complex neural patterns and their relationship to behavior. He noted that while current technology can predict behavior in simple organisms, understanding more complex brains remains a significant challenge that AI could help solve. "Overall, neuroscience is very excited about AI," Patapoutian said. It could help scientists make headway in understanding the brain because "despite decades of research, we still really don't understand how the brain works." Even after looking at the pattern of neurons firing in a brain, it remains difficult to predict what behavior is going to come next. Perhaps for a simple creature like the C. elegans worm that only has 300 neurons, it is possible, Patapoutian said. But for anything more complex, "we have absolutely no idea, and that has been one of the Holy Grails of neuroscience, not just to predict behavior, but (also) more complex thoughts, intelligence, consciousness." Hassabis said in this sense AI has come full circle. The structure of the brain inspired AI's neural networks, and now AI can help scientists understand how the brain works. Hassabis said the next frontier for AlphaFold3 is to determine how mutations can cause changes in the structure and function of the protein. AlphaFold2 solved the problem of a static protein, but he pointed out that proteins are not static. Eventually, he sees the advent of personalized medicine, where a drug is optimized for each individual's own metabolism. Looking ahead, Hassabis outlined his vision for a "virtual cell" simulation that could revolutionize biological research. Patapoutian said traditionally, the way one finds a protein structure is to pull it out of a cell. But then you don't know its natural position in the cell. Seeing the cell as a whole and where proteins are located would be more informative, Patapoutian added. For example, being able to view a protein in a cell could let you see that it is, say, localized at the tip of the neuron where specific activity is going on, Patapoutian said. "That's going to give you a very different understanding than just levels of expression, for example." Patapoutian also wondered where Hassabis procured the data to train his AI models. Hassabis said beyond the public datasets, there are companies you can hire to generate specialized data to fill in the gaps. His team also uses synthetic data they generate themselves. On the flip side, they could also develop algorithms that need less data to train on. This is to mimic human beings, who can generalize based on a few examples. Asked when artificial general intelligence (AGI) will arrive, Hassabis said people who say it will arrive in months might have an ulterior motive of needing to raise funding. "AI is over-hyped in the near term, but I think it's still under-appreciated in the medium- to long-term." He believes AGI could come in five to 10 years, but a couple of major breakthroughs need to arrive first. These breakthroughs are a fully reasoning and planning skillset, and the ability to truly be creative, not just mimic artistic styles or novel ideas it has been trained on, Hassabis said. "Could you come up with general relativity like Einstein did based on the knowledge that he had at the time in the 1900s? I don't think any of our systems could do anywhere close to that."

Europe accelerates AI drug discovery as DeepMind spinoff targets trials this year

Big pharma wants to turn AI-created drug recipes into potentially life-saving medicines Google DeepMind spinoff Isomorphic Labs expects testing on its first AI-designed drugs to begin this year, as tech startups race to turn algorithmic magic into actual treatments. "We'll hopefully have some AI-designed drugs in clinical trials by the end of the year," the firm's Nobel Prize-winning CEO Demis Hassabis told a panel at the World Economic Forum in Davos this week. "That's the plan." The potential of AI-powered drug discovery is huge. Instead of spending years or even decades testing chemicals by hand, machine learning algorithms can sift through mountains of data to spot patterns and predict which molecules could make the next miracle drug. This could lead to faster drug development, cheaper costs, and new cures. By one estimate, there are over 460 AI startups currently working on drug discovery, of which over a quarter come from Europe. Globally, more than $60bn has been invested into the space so far, and the funding flood isn't showing any signs of letting up. Yet discovering the drugs is merely one step in the process. it's only when big pharma decides they're worth manufacturing, marketing, and distributing that it'll make a real difference to the likes of you and me. That's what makes some of the recent hookups between pharma behemoths and AI startups particularly exciting. Last year, Isomorphic Labs inked a $45mn deal with Eli Lilly to collaborate on AI-based research into small molecule therapeutics. Under the agreement, Isomorphic is also eligible to receive up to $1.7bn in "performance-based milestones." The company also signed a similar collaboration with Swiss biotech Novartis. "We're already working on real drug programs," Hassabis told Bloomberg Television in an interview shortly following the announcements. "I would expect in the next couple of years the first AI-designed drugs in the clinic." Exscientia, which spun out from Dundee University in 2012, was among the first to apply AI to drug discovery. In 2024, the company advanced its first AI-designed drug candidate into human clinical trials, achieving this milestone in just 12 months -- a process that typically takes around five years. US rival Recursion acquired the Oxford-based company for $688mn in November. These are two big examples of an AI-driven drug discovery market that's booming, and increasingly, consolidating. However, there are also plenty of early-stage companies working on more niche applications of the technology. These include Cambridge, UK-based CardiaTec, which is using AI to find new drugs to treat heart conditions, and London-headquartered Multiomic Health, which is working on formulas to treat metabolic diseases. Despite all the potential though, AI isn't a silver bullet for drug discovery. While it can drastically speed up finding the right compounds needed to make new drugs, the most time-consuming steps -- like wet lab tests with physical samples, clinical trials, and FDA approvals -- aren't going anywhere. Still, AI's real power lies in that critical first phase: zeroing in on targets that might've otherwise slipped through the cracks, saving researchers time and possibly even unlocking new treatments.

DeepMind Expects Clinical Trials for AI-Designed Drugs This Year

Google DeepMind spinoff Isomorphic Labs expects research of drugs it designed using artificial intelligence will begin this year, according to its Nobel Prize-winning chief executive officer. "We'll hopefully have some AI-designed drugs in clinical trials by the end of the year," Demis Hassabis, who leads both Alphabet Inc. subsidiaries, said on a panel at the World Economic Forum in Davos. "That's the plan."