Closing the gap between AI biology and human testing with Polyphron

An important company to reach the promise of what we know AI can unlock in bio. The thing about “physical AI” is that the physical is incredibly hard and doesn’t often go the way a simulation suggests. Polyphron will help unlock incredible things for bio with what they are building. Excited for Matthew Osman and the team to get after this impactful mission!

Tumor Research Just Got over 500% Faster There is a lot of conversation right now about what AI could take away from us. This is an example of what it can give back. Dayhoff Technologies Inc. and AMD reduced a complete single-cell tumor-classification workflow from 76.1 minutes to 11.6 minutes. That means researchers can move from waiting on computation to actively iterating on the science within the same working session. They can test more hypotheses, compare more results, analyze larger datasets, and make better use of the infrastructure they already have. That matters because every bottleneck removed from bioinformatics creates more room for discovery. AI and accelerated computing are not valuable simply because they make something faster. Their real value is in what that speed enables: lower computational burden, more efficient research, and a shorter path between biological questions and meaningful answers. This benchmark is not a clinical diagnosis, and it does not mean cancer can suddenly be diagnosed in 11.6 minutes. But it does show what becomes possible when advanced computing is applied to one of healthcare’s hardest problems. This is the future Dayhoff is working toward: AI that helps scientists move faster, use resources more efficiently, and ultimately build better tools for the people who need them. Want all the technical details? Read our full blog. https://lnkd.in/gui5Pu_T

Really excited about this kind of innovation. Faster tech means faster data for critical research and that has the power to save lives.

📢 New AI Tool Flags Potentially Misassigned Tautomers in Drug Discovery Data Getting a molecule's exact atomic structure right matters more than it might seem: different tautomers, versions of the same molecule with hydrogens arranged differently, can change how it binds to a protein target. NYU researchers, publishing in Chemical Science, built an open-source AI tool called Tautomer-Predictor to catch these easily overlooked errors at scale. 🧪 The team trained a graph neural network on over 1.1 million tautomeric states mined from the Cambridge Structural Database, an underused source of experimental crystallography data, letting the model predict stable tautomers directly from 2D structures without costly quantum-mechanical calculations. Applied to 5,075 PDBbind ligands, it identified roughly 2.5% as potentially misassigned and proposed chemically plausible alternatives. The tool also processed Enamine's 4.6-million-compound library in just 3.2 hours on a single GPU node. By correcting tautomer errors upstream, the tool could meaningfully improve the reliability of molecular docking and virtual screening pipelines widely used across drug discovery. In our view, small structural details like this are often where computational drug discovery quietly loses accuracy. 💡Follow BioTech Radar for more updates! Source: Genetic Engineering & Biotechnology News (GEN), find the full article in the comments section. #BioTechRadar #DrugDiscovery #AI #Cheminformatics #MolecularModeling #ComputationalChemistry #MachineLearning

How AI Is Changing Biomedical Research. Artificial Intelligence is transforming biomedical research by helping scientists analyze complex biological data, identify patterns, and accelerate discovery. From drug discovery and protein structure prediction to medical imaging, genomics, and AI-assisted microscopy, researchers can now process enormous datasets faster and explore possibilities that would be difficult to evaluate manually. AI is also supporting: 1. Faster identification of potential drug candidates 2. More efficient analysis of genomic and molecular data 3. Improved interpretation of biomedical images 4. Prediction of disease-related patterns 5. More personalized approaches to research and treatment development However, AI is not replacing researchers. Its greatest value comes from combining computational intelligence with human scientific expertise, validation, and critical thinking. The future of biomedical research will increasingly depend on collaboration between scientists, clinicians, data experts, and AI systems. AI may accelerate the discovery process, but human expertise remains essential to turning discoveries into meaningful scientific progress. #AI #BiomedicalResearch #HealthcareAI #MedicalResearch #Biotechnology #DrugDiscovery #AiMedicalMixer

Anthropic Quietly Expands Into AI Drug Discovery With New Biology Lab Anthropic, the company behind Claude AI models, is reportedly expanding its research ambitions beyond software and artificial intelligence systems by establishing a new biology-focused laboratory. The move signals the company’s growing interest in using AI for scientific research, including areas such as drug discovery, biological analysis and complex scientific problem-solving. AI companies are increasingly exploring healthcare and life sciences, where advanced models could help researchers analyse large biological datasets, identify potential drug candidates and accelerate parts of the research process. Anthropic’s expansion into biology comes as competition in the AI industry moves beyond chatbots and productivity tools toward specialised applications in science, medicine and engineering. The company has not publicly disclosed full details about the lab’s structure, research programmes or timelines, but the development reflects a wider industry shift toward AI-powered scientific discovery. Follow TechAmerica.ai for the latest technology news, AI breakthroughs and innovation updates.

If AI increasingly tells us where to look, what happens to discoveries we weren’t looking for? A thought-provoking piece in Nature Cell Biology, “Scientific culture in the age of AI,” raises an important question for the future of biomedical research. As AI becomes increasingly embedded in biomedical research, the question is no longer whether AI will transform how we do science, it already is! AI can navigate enormous datasets, predict structures, design molecules, and generate hypotheses at unprecedented scale. History tells us great science is more than prediction. Transformative discoveries often emerge from curiosity, unexpected observations, and challenging established assumptions. So, what happens to serendipitous discovery when algorithms increasingly drive hypothesis generation? As AI becomes deeply integrated into drug discovery and cell biology, how do we balance predictive models with human intuition and wet-lab experimentation? Perhaps maintaining a culture of curiosity, rigorous experimental validation, and cross-disciplinary thinking will be just as important as the technology itself. https://lnkd.in/gKG2zFfc

💡Check out this Editor’s Choice article: 🫀 A Deep Learning Methodology for Screening New Natural Therapeutic Candidates for Pharmacological Cardioversion and Anticoagulation in the Treatment and Management of Atrial Fibrillation by Dong, T., et al. (2025). Biomedicines, 13(6), 1323. Artificial intelligence is opening new possibilities for drug discovery, including in settings with limited resources. A recent study published in Biomedicines explored how a low-cost deep learning approach could help identify natural compounds with potential relevance to the treatment and management of atrial fibrillation. Key areas of the study address: • The use of a deep learning approach to support drug discovery for atrial fibrillation. • The identification of natural compounds as potential therapeutic candidates. • Approaches to drug discovery that can be used in settings with limited computing resources. • The potential role of machine learning in expanding access to cardiovascular research and innovation. 📖 Read the full text: https://brnw.ch/21x5xbx #MDPI #OpenAccess #Biomedicines #DeepLearning #Bioinformatics #DrugDiscovery #Transcriptomics #Proteomics #AtrialFibrillation #MachineLearning

Absolutely a must read. This comment nailed down why AI will never replace natural intelligent and why academia and pharma industry should keep the balance.

This is highly interesting food for thought. We must not forget that some of the greatest breakthroughs in science happened due to serendipity and natural intelligence, discoveries I don't think AI could have ever predicted. One example is the accidental discovery of heavy-chain-only antibodies in camelids