Latest news with #Recursion
Yahoo
4 days ago
- Business
- Yahoo
AI in drug discovery: from hypothetical to reality
AI has been a buzzword in the pharmaceutical industry for almost a decade, with countless headlines promising to revolutionise how drugs are discovered, developed, and deployed. The hype has typically outpaced reality; however, with a growing number of AI-generated drug candidates entering clinical trials, we find ourselves at a crucial potential turning point for the field. As the first AI-designed molecules begin clinical trials, the industry is watching closely to see if AI can deliver novel, successful therapies or if it will remain a supporting tool in an already complex research process. AI is not a single technology, but a broad set of computational tools that can support nearly every stage of drug development. In early discovery, AI can help identify novel targets, generate new chemical compounds, predict how drugs will interact with proteins, and optimise lead compounds (promising compounds that show potential as an effective drug) for desirable properties. In later stages, it is increasingly used to select patients for clinical trials, repurpose existing drugs, and predict adverse effects. Early attention focused on the ability of generative AI models, particularly deep learning and reinforcement learning frameworks, to create candidate compounds faster than any human chemist, with a theoretical ability to explore vast chemical spaces more efficiently than traditional methods. AI-designed candidates used to remain confined to in silico (computational) experiments or the preclinical stage. That is now changing. In the past two years, several AI-designed drugs have advanced into human trials, providing the first real-world test of these technologies in the clinic. Insilico Medicine, a Hong Kong- and New York-based AI biotech company, gained attention in 2023 when it announced that its drug candidate INS018_055, developed using its proprietary platform, had entered Phase II trials (testing effectiveness and safety). The compound was designed from scratch using generative models trained on structural data, an early confirmation that AI can do more than just screen libraries. Other companies such as BenevolentAI, Recursion, Schrödinger, and Relay Therapeutics are advancing candidates identified, optimised, or prioritised using AI tools, though not all are strictly 'AI-generated'. Importantly, these companies are not just discovering molecules but also positioning themselves as drug developers and strategic collaborators. Big pharma has increasingly embraced AI, using partnerships, joint ventures, and acquisitions to reduce risk in target selection and accelerate early-stage drug development. A prime example is this deal between Sanofi and Exscientia, worth up to $5.2bn, focused on AI-designed small molecules across oncology and immunology. Furthermore, AstraZeneca has been collaborating with BenevolentAI to identify new drug targets in chronic kidney disease and fibrosis. Pfizer, Bayer, Merck, and Roche have all partnered with AI-native biotechs or built their own internal capabilities, often focused on areas such as rare diseases, central nervous system disorders, or target deconvolution. These collaborations reflect a broader shift in mindset: AI is increasingly seen not as a competitor to traditional research and development (R&D), but as a strategic enabler, using human expertise to improve accuracy and compress timelines in high-risk areas. The core value proposition of AI in drug discovery is speed and efficiency. Drug development typically costs over $2bn and can take 10-15 years per new drug. AI promises to shorten the time from target identification to candidate nomination by rapidly generating hit compounds, improving target-disease linkage accuracy, and selecting better patient populations for trials. Insilico, for example, claimed that INS018_055 progressed from target discovery to Investigational New Drug filing in under 30 months, significantly faster than industry averages. However, real-world validation of these efficiency claims is still pending, and it remains unclear whether AI shortens timelines in later-stage development, where most costs and failures still occur. Moreover, regulatory pathways for AI-designed drugs are still evolving. While the molecules themselves follow industry standards, questions remain around intellectual property ownership, algorithm transparency, and validation of model-generated hypotheses. The excitement around AI in drug discovery has driven enormous investor interest, bringing volatility along with it. Many AI-first biotech companies raised substantial capital through initial public offerings or special-purpose acquisition companies (SPACs) deals during the 2021-22 biotech boom. Yet several have seen share prices decline sharply as investor enthusiasm met the realities of long development timelines, modest clinical progress, and uncertain monetisation strategies. BenevolentAI, for example, went public via a €1.5bn ($1.8bn) SPAC in 2022 but lost over 70% of its value by mid-2024. Recursion, despite its ambitious data-driven drug discovery platform and partnerships with Bayer and Roche, has also faced pressure from shareholders seeking faster returns. Despite this, the field is still maturing. Investors and pharma partners are shifting from broad platforms to more focused evaluations based on data-driven productivity, clinical progress, and pipeline value. The industry is now approaching a key milestone: the first regulatory approval of an AI-designed drug. If INS018_055 delivers positive Phase II data, it would mark a transformative moment, validating AI not just as a tool but as a source of new and unique therapies. Looking further ahead, AI could diversify drug development by enabling smaller companies to design high-quality molecules without vast lab infrastructure. It may also unlock progress in rare diseases, neglected indications, or emerging pathogens, where traditional business models struggle to justify investment. Beyond small molecules, AI is also being explored in biologics design, protein engineering, messenger ribonucleic acid optimisation, and clinical trial design, suggesting its impact could extend across the entire pharmaceutical value chain. AI is no longer just a futuristic concept in pharma; it is producing real assets, forming strategic alliances, and slowly earning its place in the clinical pipeline. However, expectations remain high, and the burden of proof now rests on human trials, not machine models. As the first wave of AI-designed drugs enters mid-stage development, the industry will be watching not just for approvals, but for evidence that AI can improve outcomes, accelerate timelines, and reduce costs in a field where failure is still the norm. If successful, the next decade may not just belong to AI, it may be designed by it. "AI in drug discovery: from hypothetical to reality" was originally created and published by Pharmaceutical Technology, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. 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Zawya
24-06-2025
- Health
- Zawya
MIT Jameel Clinic and CSAIL launch new AI model accelerating the future of drug discovery ‘Boltz-2'
Cambridge, Massachusetts – The Jameel Clinic, the epicentre of artificial intelligence (AI) and health at the Massachusetts Institute of Technology (MIT), announced today the release of Boltz-2 — a groundbreaking artificial intelligence model which will transform the speed and accuracy of drug discovery. The announcement was made together with the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the biotechnology company Recursion. Boltz-2 breaks new ground by jointly modelling both structure and binding affinity, a critical parameter in small molecule drug discovery. A big leap for small molecule drug discovery Boltz-2 builds on the success of Boltz-1, a pioneering model first released in 2024 that can determine protein structures, by adding a powerful new ability: accurately predicting how strongly a drug molecule will bind to a target protein — a crucial factor in determining its effectiveness. In doing so, Boltz-2 addresses one of the most complex challenges in early-stage drug development. Boltz-2's affinity module was trained on millions of real lab measurements, showing how strongly different molecules bind to proteins. Thanks to this, Boltz-2 can now predict binding strength with unprecedented accuracy across several benchmarks reflecting different stages of real-world drug discovery. Boltz-2's predictions come very close to those produced by full-physics free energy perturbation (a precise computer simulation that predicts how strongly a drug sticks to its target, but that can take up to a day to run one test even on a GPU) – at over 1,000 times the speed. It is the first deep learning model to deliver that level of precision. Saro Passaro, researcher at the MIT Jameel Clinic and co-lead of the Botlz-2 project, said: 'This release is especially significant for small molecule drug discovery, where progress has lagged behind the rapid gains seen in biologics and protein engineering. 'While models like AlphaFold and Boltz-1 allowed a significant leap in the computational design of antibodies and protein-based therapeutics, we have not seen a similar improvement in our ability to screen small molecules, which make up the majority of drugs in the global pipeline. 'Boltz-2 directly addresses this gap by providing accurate binding affinity predictions that can dramatically reduce the cost and time of early-stage screening.' Gabriele Corso, PhD student at MIT CSAIL and one of the lead researchers behind Boltz-1 and Boltz-2, said: 'This performance increase makes Boltz-2 not just a research tool, but a practical engine for real-world drug development. 'Instead of spending hours simulating the interaction between a single molecule and its target, scientists can now screen vast chemical libraries within the same time frame, enabling early-stage teams to prioritise only the most promising compounds for lab testing.' Open-source and optimised for medical research The Boltz-2 model also introduces a new feature, Boltz-Steering, which refines molecular structure predictions and make them more realistic. This allows researchers to guide the model using experimental data, example structures, or design goals — giving them greater control and customisability in their search for new treatments. Boltz-2 will be released as a fully open-source model under the MIT licence, including the code, weights and training data, enabling researchers around the world to freely access and build upon its capabilities. A breakthrough for the MIT Jameel Clinic and CSAIL The model represents a major milestone in an ambitious research programme launched in early 2023 by the MIT Jameel Clinic and CSAIL. The team set out to develop a machine learning system that could not only predict the 3D shape of proteins — like AlphaFold — but also understand how and why molecules interact, as well as how likely they are to bind to each other. This deeper understanding is essential for designing effective new therapies, particularly for diseases caused by molecular dysfunction. Boltz-1, released in 2024, was the first result of that effort. Created as a fast, accessible alternative to AlphaFold3, Boltz-1 quickly became the most widely adopted open-source tool of its kind, used by thousands of scientists across academia, biotech startups, and pharmaceutical companies. It demonstrated that open and interpretable models could rival the best in the field. Now, with Boltz-2, the MIT team is taking the next step — targeting small molecule drug discovery, an area that has historically lagged behind biologics and protein engineering in terms of computational tools. Boltz-2 is the latest milestone in MIT Jameel Clinic's growing portfolio of open-source tools for health, developed at the intersection of AI and medicine — and part of a broader mission by the Jameel Clinic to make cutting-edge technology accessible for solving the world's most pressing health challenges. The team includes MIT Jameel Clinic AI faculty lead Professor Regina Barzilay; MIT CSAIL principal investigator Professor Tommi Jaakkola; PhD students Gabriele Corso and Jeremy Wohlwend; MIT Jameel Clinic researcher Saro Passaro; as well as additional collaborators from Recursion. About Jameel Clinic: The Jameel Clinic is the epicentre of artificial intelligence (AI) and healthcare at MIT. It works to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalisation and management. Building on MIT's pioneering history in artificial intelligence and life sciences, the Jameel Clinic works on novel algorithms suitable for modelling biological and clinical data across a range of modalities including imaging, text and genomics. While achieving this goal, the team strives to make new discoveries in machine learning, biology, chemistry and clinical sciences. The Jameel Clinic was co-founded in 2018 by MIT and Community Jameel, the independent, global organisation advancing science to help communities thrive in a rapidly changing world. About Community Jameel: Community Jameel advances science and learning for communities to thrive. An independent, global organisation, Community Jameel was launched in 2003 to continue the tradition of philanthropy and community service established by the Jameel family of Saudi Arabia in 1945. Community Jameel supports scientists, humanitarians, technologists and creatives to understand and address pressing human challenges in areas such as climate change, health and education. The work enabled and supported by Community Jameel has led to significant breakthroughs and achievements, including the MIT Jameel Clinic's discovery of the new antibiotics Halicin and Abaucin, critical modelling of the spread of COVID-19 conducted by the Jameel Institute at Imperial College London, and a Nobel Prize-winning experimental approach to alleviating global poverty developed by the co- founders of the Abdul Latif Jameel Poverty Action Lab at MIT.
Forbes
06-06-2025
- Science
- Forbes
The Prototype: This AI Model Could Make It Faster To Find New Medicines
In this week's edition of The Prototype, we look at a new AI model that could speed up drug discovery, how the Trump/Musk blowup could impact NASA, a new class of electronics and more. You can sign up to get The Prototype in your inbox here. getty The 2024 Nobel Prize in Chemistry was awarded in part to Deepmind's Demis Hassabis and John Jumper for the development of AlphaFold–an AI model that predicts the structure of proteins, the complex chemicals essential to making our bodies work. Since its inception, this model and others like it have been put to use in laboratories around the world, enabling new biological discoveries. Now a team from MIT and pharmaceutical company Recursion, with support from Cancer Grand Challenges, have developed a tool that takes these principles further–and may help researchers find new medicines more quickly. Called Boltz-2, this open-source generative AI model can not only predict the structure of proteins, it can also predict its binding affinity–that is, how well a potential drug is able to interact with that protein. This is crucial in the early stages of developing a new medicine. Until now, scientists could only find binding affinity in one of two ways: they could actually conduct an experiment to determine it, or they could use a complicated computer simulation process called FEP. In a paper published today, which has not yet been peer-reviewed, the team demonstrated that it could produce similar results to an FEP–but significantly faster. 'Boltz-2, in just 20 seconds, can match the performance of FEP, which usually takes from 6-12 hours,' said researcher Gabriele Corso. 'Pretty much changing the game.' Getty Images SpaceX has been caught in the crossfire of the ongoing feud between Donald Trump and company founder Elon Musk. The two men have been sharing barbs over the President's proposed budget bill, with Musk criticizing it for including too much spending and increasing the deficit. On Thursday afternoon, the President posted on Truth Social that '[t]he easiest way to save money in our Budget, Billions and Billions of Dollars, is to terminate Elon's Governmental Subsidies and Contracts.' If Trump were to follow through on cancelling contracts, the biggest price may well be paid by NASA. Although the space agency played a crucial role in getting the company off the ground, SpaceX doesn't need it anymore. According to Musk, the company is currently bringing in around $15.5 billion a year in revenue. Forbes estimates that about 80% of this comes from its internet business, Starlink. And while SpaceX still gets plenty of government business, it also launches dozens of commercial spacecraft every year. The reverse, however, isn't true. NASA relies heavily on SpaceX for its operations–the company's rockets launched more than half of the agency's space missions last year. And while NASA has other partners in aerospace, such as Boeing, many are years behind SpaceX in terms of development. Read the whole story here. A team of researchers at Virginia Tech invented a new kind of circuit board that is both more durable and easier to recycle than conventional electronics. It's composed of a soft plastic that's embedded with a liquid, conductive metal to carry electricity. The resulting electronics work even if they're bent out of shape and can even self-repair some damage. For recycling, they can be chemically deconstructed with a simple process that makes it easy to re-form into a new product. Japanese space startup Ispace's second attempt to land a spacecraft on the Moon failed this week. According to the company, the laser rangefinder that its spacecraft used to measure the distance to the surface experienced communications difficulties. Because it didn't know its altitude, it didn't slow down enough for a safe landing, causing it to crash. In my other newsletter, InnovationRx, Amy Feldman and I looked at the impact of Trump's proposed budget cuts on biomedical research and global health, news from the ASCO cancer meeting and a biotech company making drug products through fermentation. Solar panels provide an unexpected environmental benefit–when they're placed in drought-prone grasslands, they boost soil moisture levels and increase plant growth by 20% compared to open fields, because of both the shade they provide and water that collects on them. A new compound, called infuzide, showed antibacterial activity against strains that are resistant to antibiotics, which may provide a new weapon for doctors against infectious diseases. Amazon is reportedly testing humanoid robots to see if they can be used to deliver packages. The retail giant has already been putting similar technology to work in its warehouses. Researchers found that diatoms, a kind of algae with silica in its cell walls, could be introduced to the Moon's soil to make it capable of growing crops. If you're in midlife and wondering if you should abandon your morning coffee, think twice–at least, if you're a woman. That's because a new analysis, which followed nearly 50,000 women for over 30 years, found that those who drank coffee (the good stuff, with caffeine) were more likely to age in a healthy way, maintaining both their physical and cognitive health across a wide variety of parameters, than those who drank tea or decaf. As a middle-aged dad, two things I greatly enjoy are hard rock music and military history. Swedish metal band Sabaton scratches both of those itches at the same time by singing heavy ballads about historic battles and the people who fought them. Some of my favorite tracks of theirs include 'Night Witches' (about an all-female Soviet bomber regiment in World War II), 'The Last Stand' (about the Swiss Guards who defended Rome in battle in 1527), "Blood of Bannockburn" (about a major victory in the War of Scottish Independence) and 'To Hell And Back' (about American World War II hero Audie Murphy). They're like Schoolhouse Rock but with much better guitar solos.
Yahoo
11-05-2025
- Business
- Yahoo
Why Recursion Pharmaceuticals Stock Plummeted 24% This Week
The results of a recent survey released Tuesday showed President Trump's actions are likely to make it harder for biotech companies like Recursion to raise capital. The company reported less-than-stellar earnings yesterday and announced it was ending the development of a significant portion of its pipeline. 10 stocks we like better than Recursion Pharmaceuticals › Shares of Recursion Pharmaceuticals (NASDAQ: RXRX) fell this week. The stock lost 24% as of market close on Friday. The move comes as the S&P 500 and Nasdaq Composite both slipped slightly. Recursion revealed disappointing first-quarter earnings on Monday and announced it would pare down its development pipeline. Recursion was also hit by a survey revealing that the biotech industry expects President Donald Trump's federal research cuts will make raising capital more challenging. Recursion reported an earnings-per-share (EPS) loss of $0.50 on sales of $14.75 million. While the former ever so slightly beat Wall Street's expectations, the top-line figure was below the forecast of $14.98 million. The company also announced it was ending research on a significant portion of its pipeline in order to cut costs. The Trump administration has taken aim at the National Institutes of Health (NIH), which provides research dollars for biomedical research, as well as major research universities and other science organizations. A survey released Tuesday revealed that a majority of biotech leaders polled believed these cuts would make raising capital harder. Recursion is still heavily investing in research and development and operates deep in the red. It will likely need outside funding to continue long-term and reach a point where its investments pay off. While Recursion's novel use of AI is promising and could lead to lucrative breakthroughs, there is a lot of uncertainty here. This is definitely a stock for aggressive, risk-tolerant investors. If that's you, Recursion could pay off, but it will take time, and there are no guarantees. Before you buy stock in Recursion Pharmaceuticals, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Recursion Pharmaceuticals wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $617,181!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $719,371!* Now, it's worth noting Stock Advisor's total average return is 909% — a market-crushing outperformance compared to 163% for the S&P 500. Don't miss out on the latest top 10 list, available when you join . See the 10 stocks » *Stock Advisor returns as of May 5, 2025 Johnny Rice has no position in any of the stocks mentioned. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy. Why Recursion Pharmaceuticals Stock Plummeted 24% This Week was originally published by The Motley Fool
Yahoo
07-05-2025
- Business
- Yahoo
Why Recursion Pharmaceuticals Stock Was Getting Mashed on Monday
Key Points The company published its latest quarterly earnings report this morning. It continues to draw limited revenue, while its costs are growing notably. Recursion Pharmaceuticals (NASDAQ: RXRX) didn't have a fine start to the working week on Monday, at least as far as its stock was concerned. Investors traded out of it following the clinical-stage biotech's first-quarter earnings release and business update, and the share price was down by 15% in mid-session trading. The S&P 500 index was doing comparatively better, with a 0.3% decline. Image source: Getty Images. Where to invest $1,000 right now? Our analyst team just revealed what they believe are the 10 best stocks to buy right now. Continue » Light revenue and a deepening net loss For the quarter, Recursion -- which although a clinical-stage company earns some coin from collaboration agreements with large pharmaceutical companies -- booked just over $14.7 million in revenue. That topped the nearly $13.8 million in the same period of 2024. Operating costs nearly doubled across that stretch of time, however, resulting in a much deeper net loss. Recursion's bottom-line shortfall worsened to more than $202 million ($0.50 per share) against the year-ago quarter's $91 million deficit. Both headline numbers missed analyst estimates, if not by much. On average, pundits tracking Recursion stock anticipated slightly under $15 million for revenue, and a $0.49-per-share net loss. Aiming for the stars with AI The company is notable for being a biotech that has actively and enthusiastically embraced artificial intelligence (AI) technology to discover and develop new drugs. It is currently developing treatments mainly for different types of cancer, but also has a pair of rare disease programs. Yet none of these have yet advanced to later stages, and investors might be hungry for more progress at this point. That being said, AI in medicine is still quite a new factor, and if utilized effectively could speed up both the discovery and development processes significantly. At this point Recursion is a stock for investors willing to accept a significant degree of risk; however, the payoffs could eventually be considerable. Should you invest $1,000 in Recursion Pharmaceuticals right now? Before you buy stock in Recursion Pharmaceuticals, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Recursion Pharmaceuticals wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years.
