AI in Molecular Prediction

We are proud to present our latest paper on physics-informed AI for drug design appearing in PNAS special issue on machine learning in chemistry . Standard data-driven AI does not work well on examples that are significantly different from training data. This can result in unphysical predictions that are clearly wrong. To limit this type of unphysical result in the realm of drug design we introduced a new machine learning model called NucleusDiff, which incorporates a simple physical idea into its training, greatly improving the algorithm's performance. NucleusDiff ensures that atoms stay at an appropriate distance from one another, accounting for physical concepts such as repellant forces that prevent atoms from overlapping or colliding. Rather than accounting for the distance between every single pair of atoms in a molecule, which would be expensive, NucleusDiff estimates a manifold, and on that manifold, it then establishes main anchoring points to watch, making sure that the atoms never get too close to one another. We predicted binding affinities of a newer molecule that was not included in the training dataset: the COVID-19 therapeutic target 3CL protease. NucleusDiff showed increased accuracy and a reduction of atomic collisions by up to two-thirds as compared to other leading models.

Half a million genomes. 1.5 billion variants. One breakthrough: we are all truly unique. Twenty years ago, the Human Genome Project took 13 years and $2.7B to sequence a single genome. Today? We can sequence a genome in less than 24 hours for under $1,000. Last week, UK Biobank released 490,640 whole genomes — the largest genetic dataset ever (Nature, 2025). What did we learn? • Each person carries 4–5 million variants • 76% appear in fewer than 10 people — your genome is almost entirely yours • 1 in 10 carries clinically actionable mutations where doctors can intervene today (e.g., BRCA1/2 for cancer, LDLR for heart disease) Why it matters: • Previous genetic tests captured ~6% of human variation. This dataset reveals 40× more • In non-coding regions — the biological switches controlling genes — researchers found 63 new disease associations • Adding 31,785 non-European genomes uncovered 82 disease links invisible in Eurocentric studies From genetics to health impact This transforms medicine today: • Prevention - Polygenic risk scores flag disease decades before symptoms • Diagnosis - Rare disease patients waiting years for answers finally find them • Treatment - Pharmacogenomics matches the right drug, right dose, to your genome The next frontier: genetics + everything else Genetics is the hardware. Health is the software running in real time. Your DNA is fixed, but biology is dynamic, shaped by: • Epigenetics: how environment and lifestyle switch genes on/off • Proteomics & metabolomics: molecular signals revealing your current health state • Digital biomarkers: continuous data from stress, sleep, glucose, heart rate • Stress biology & neuroendocrine signaling: how cortisol and brain-body responses reshape your health trajectory Layer these dynamic signals onto genetic foundations, power them with AI, and you create living health models, not just predicting disease, but understanding when, why, and how it manifests in YOU. The critical question? We've spent decades treating the "average patient" — who doesn't exist. Now we can better see each person as they truly are: biologically unique, dynamically changing, infinitely complex. The healthcare winners of the next decade won't just collect data: they'll integrate genetics, epigenetics, molecular and phenotypic tests, lifestyle, stress biology, and digital signals to deliver truly personalized, preventive care at scale. There is no "normal" genome, only 8 billion unique experiments in being human. And we just decoded the first half million. 👉 Which excites you more: knowing your genetic blueprint, or understanding how your daily choices rewrite it?

For 50 years, a key protein behind heart disease, among the leading cause of death worldwide remained a scientific mystery. It was too large and complex for traditional methods; its structure was invisible to us. Now, researchers have combined cryo-electron microscopy with DeepMind's AlphaFold to reveal the atomic structure of that protein: apoB100, the very scaffold of "bad cholesterol." This marks a deeper shift in how we approach science.  When we can see biology at this level of detail, healthcare moves from managing symptoms to engineering interventions at the molecular root. AI starts to function as a new kind of microscope, one that reveals the invisible machinery of life and allows entirely new questions to be asked. This is the kind of progress that matters.     AI as an instrument for understanding, precision, and prevention. It’s a glimpse into a future where compute and science converge to tackle humanity’s hardest health challenges at their source.    Read the full story: https://lnkd.in/gbum2dKu #AIInHealthCare #AIForGood

I’m often asked where I see AI make a tangible, real impact in the world today. To that, I answer with #AlphaFold, the revolutionary AI model from Google DeepMind, that is able to predict the structure of a protein simply from its amino acid sequence. 5 years ago, AlphaFold solved the 50-year grand challenge of protein folding, followed by the equally meaningful decision to make 200 million protein structures freely available to the scientific community. Since then, Demis Hassabis and John Jumper have been recognized with a Nobel Prize for their work on AlphaFold, and we see over 3.3 million users of it globally, with more than a third of users right here in Asia-Pacific. Here is just a snapshot of those applications: 🔬 Dr. Su Datt Lam at the National University of Malaysia (UKM) is learning more about Melioidosis to better fight the silent killer. 🧬 Researchers Lim Jackwee lim and Yinxia Chao at Singapore’s A*STAR - Agency for Science, Technology and Research and National Neuroscience Institute (NNI) are visualizing proteins linked to Parkinson’s. 🔍 Professor Ji-Joon Song’s team at the Korea Advanced Institute of Science and Technology lead to cancer and other diseases. 🪢Dr. Danny Hsu at Academia Sinica, Taiwan is advancing our understanding of exceptionally complex protein “knots”. ♨️ Dr. Syun-ichi Urayama’s team is uncovering new evolutionary insights from microbes in Japan’s hot springs! Listen to one of their stories below, and read more about all of them here: https://lnkd.in/d7wyACpK #GoogleDeepMind #AIforGood 

NEW: AI models are creating antibodies that look more and more like viable drug candidates. Nabla Bio's latest results, posted Wednesday in a preprint, demonstrate its newest AI model called JAM-2 generated de novo antibodies with strong binding affinities and key developability criteria. "The barrier to creating an antibody is increasingly moving to zero," CEO Surge Biswas told me, which could help fuel longtime industry dreams like personalized therapeutics or designing biologics that won’t trigger immune responses. This research was also done by a four-person team, working on 16 targets in parallel. Nabla estimated the same amount of work would take six months for an 80-person team using traditional pharma methods. That “enormous compression of work” could open up new ways of thinking about antibody design's impact on R&D, broadly, president Frances Anastassacos said. My latest for Endpoints News: https://lnkd.in/ePKSBwH6

For the first time, a machine can look at a routine mammogram and predict a woman's 5-year breast cancer risk quite accurately: no history, no demographics, just the image itself revealing invisible patterns. As someone working inside the pharmaceutical ecosystem, this is interesting and exciting. This is the kind of shift that makes you rethink everything you assumed about early detection. Until now, doctors estimated risk using age, family history, and breast density. Useful signals, but broad, indirect, and often late. We've been predicting population risk far better than individual risk. That is now changing. An image-only AI model can predict 5-year breast cancer risk more accurately than breast density alone. The image reveals patterns the human eye cannot see. This is AI potentially adding healthy years to people's lives. Years without chemotherapy, without surgery, without the fear of late discovery. We're starting to see risk while the body still looks normal. From an industry lens, this changes three fundamentals: •⁠ ⁠Screening shifts from age-based to risk-based. Screening frequency and prevention strategies can now be customized based on actual biological risk, not age brackets. •⁠ ⁠Prevention becomes earlier and more precise, but raises hard questions about false positives, patient anxiety, and long-term follow-up. •⁠ ⁠We face a scale challenge. AI can identify risk at the population level. Healthcare systems must be ready to act without overwhelming clinicians or excluding low-resource settings. The technology is moving faster than our operating models. The real leadership test is no longer whether AI can predict risk. It's whether we can deploy it responsibly, equitably, and at scale without creating gaps in care. The future of oncology will not be defined only by better drugs. It will be defined by how early we dare to see risk and how wisely we choose to act on it. #AI #healthtech

Five years ago, AlphaFold solved the protein structure prediction problem at CASP14, cracking a 50-year grand challenge in biology. It has been an absolute honour and privilege to have been part of this journey alongside Demis and John. Over 3 million researchers across 190 countries have since used AlphaFold to predict the structure of more than 200 million proteins. The impact spans from revealing apoB100's structure, advancing heart disease research, to supporting endangered honeybee conservation in Europe. Protein structure prediction was the root node problem in structural biology. By solving it, we opened up entirely new avenues for discovery. What AlphaFold demonstrated is that AI can accelerate scientific progress when applied to the right foundational challenges. We've since expanded this approach across biology. AlphaMissense and AlphaGenome are helping researchers understand genetic mutations and disease. AlphaProteo is designing new protein binders for targets in cancer and diabetes. We're applying similar thinking to challenges in fusion energy, materials discovery and climate science. Today, we're sharing The Thinking Game, following our team through the journey that made AlphaFold possible. To understand more about AlphaFold's impact, see the blog here: https://lnkd.in/eiPSAeKc #AlphaFold #AIforScience

AI is incredibly good at optimizing. It can analyze large datasets, identify patterns, and recommend the “best” compound within a given chemical series. That’s powerful — but it’s not enough. Because optimization is not innovation. One of the biggest reasons AI hasn’t yet delivered a wave of breakthrough drugs is this: the models we’re using are largely trained to refine what's already known, not to imagine what’s possible. Most AI systems in drug discovery work by learning from historical data — known compounds, known targets, known properties. From there, they generate new molecules that are similar, but slightly improved. They might bind a little tighter. Be a little less toxic. Have slightly better solubility. This is incremental progress. And it’s valuable, especially in lead optimization. But when the industry talks about "AI discovering new drugs," we often imagine something more revolutionary: first-in-class molecules, targeting undruggable proteins, opening up new biology. That kind of leap requires creative generalization. It demands the ability to infer beyond the data it was trained on. But generative models are, by design, bound by their training data. If the model has never “seen” a class of molecules, or if a protein target lacks good structural data, the system has little foundation on which to innovate. And here’s the paradox: the more robust and curated your training data, the more your model becomes anchored in the past. So, how do we break this loop? Instead of asking AI to generate the “best” compound, we should ask it to generate diverse hypotheses — especially ones that break outside conventional chemical space. Then we need wet-lab systems and organizational cultures that are willing to test bold ideas, not just safe bets. AI’s real potential in drug discovery won’t be realized through faster optimization. It will come from enabling smarter exploration — of targets, of modalities, of mechanisms we haven’t yet charted. #drugdiscovery #AIinHealthcare #biotech #machinelearning #lifesciences #computationalchemistry #generativemodels #pharma #innovation #futureofmedicine

Biotech company Insilico Medicine is using AI to rethink drug discovery. Now, with a fresh $110 million in funding pushing its valuation past $1 billion, the startup is considering a Hong Kong IPO. Developing a new drug is notoriously slow and expensive—it can take over a decade and billions of dollars before a single treatment reaches patients. Insilico wants to change that with AI, making the process faster, cheaper, and more precise. ► At the core of Insilico’s approach is Pharma.AI, which analyzes vast biological datasets and predicts which molecules are most likely to work, reducing the need for excessive trial and error.  ► Insilico is already delivering results—its leading drug candidate, Rentosertib, for a serious lung disease, reached early clinical trials in just 2.5 years, a process that normally takes up to six.  ► The company is making big deals by licensing its AI-generated drugs to pharmaceutical giants like Sanofi and Fosun Pharma, securing $3.5 billion in contract value. The startup’s pipeline now includes 30 drug candidates, with 10 receiving clearance from the US Food and Drug Administration (FDA) to proceed with human trials. Beyond discovery, Insilico is using AI to speed up lab work. The company is testing humanoid robots in its China lab to automate repetitive tasks, collect data, and reduce human error, all in an attempt to speed up research. If Insilico succeeds, it could reshape the entire drug discovery process. By combining AI, real drug candidates, and even lab robots, the company is tackling the whole process, not just one piece of it. Faster, cheaper, and maybe even better—this could be a glimpse of how future drugs come to life. #artificialintelligence #innovation

We are nearing the limits of the known antibiotic universe. For decades, progress has largely meant revisiting familiar molecules, even as resistance continues to outpace discovery. A recent effort from Massachusetts Institute of Technology changes the nature of the search itself. Instead of screening what already exists, researchers used generative AI to design tens of millions of hypothetical compounds that have never been synthesized or cataloged before. This is not deeper exploration of known space, but the creation of entirely new chemical territory. The AI generated molecules from first principles, guided by rules of efficacy and synthesizability. Several candidates that emerged are structurally unlike existing antibiotics and appear to act through a more fundamental mechanism: disrupting bacterial cell membranes. That distinction matters. Resistance often develops against drugs targeting specific internal proteins, but compromising the membrane is a broader, harder-to-defend strategy. In early studies, one AI-designed compound proved effective against drug-resistant gonorrhea by targeting a novel membrane-related protein, while another cleared MRSA infections in animal models, operating outside known antibiotic classes. The deeper shift here is conceptual. Generative models expand discovery beyond what can be searched or screened, into what can be designed. At a time when antimicrobial resistance is a growing global threat and the traditional pipeline is stagnant, this exploration-first approach offers a credible path forward. The next chapter of antibiotic development may depend less on rediscovery, and more on invention. #ArtificialIntelligence #GenerativeAI #DrugDiscovery #AntibioticResistance #AntimicrobialResistance #ComputationalBiology #AIinHealthcare #Biotech #LifeSciences #ScientificInnovation