Bioengineering Simulation Platforms

BioEmu-1, developed by Microsoft Research, is a deep learning model that predicts dynamic structural ensembles of proteins, addressing the limitations of static models like AlphaFold and computationally intensive molecular dynamics (MD) simulations. Unlike traditional MD simultion, which struggles with scalability, BioEmu-1 combines data from AlphaFold, MD trajectories, and experimental stability metrics to generate thousands of conformations rapidly (10,000–100,000x faster) on a single GPU. It employs a diffusion-based generative approach to explore free-energy landscapes, revealing intermediate states and transient binding pockets critical for drug design. Validated against MD benchmarks, it accurately predicts folding free energies (R²=0.85) and allosteric pathways, aiding applications like kinase inhibitor development. Current limitations include handling novel folds and large multi-domain proteins, but future updates aim to integrate cryo-EM/NMR data and expand to RNA dynamics. Open-sourced to the community which is a great open source contribution to biology, BioEmu-1 accelerates research in drug discovery and protein engineering by bridging static structure analysis with dynamic functional insights. #ProteinDynamics #StructuralEnsembles #DeepLearning #AIInBiology #DrugDiscovery #Bioinformatics #MicrosoftResearch #OpenScience #MolecularDynamicsComparison #Allostery #ConformationalChanges #GenerativeAI #FreeEnergyLandscapes #CryoEM #KinaseInhibitors #ComputationalBiology #TherapeuticDesign

NVIDIA is quietly building the operating system of healthcare. Soon, we test drugs and therapies on digital twins first. For 100 years, medicine works in one main way: hypothesize → run a slow and expensive trial → wait for results → try again. Biological trial-and-error. On real people. In real time. We now stand at a new layer: Biology stays in the loop. But we move to simulation-first. NVIDIA is not building “one more tool”. It builds the stack that makes this shift possible: → Clara: the health AI platform Hospitals and startups use Clara to build models for radiology, pathology, workflows. Data in, AI models out. From image reconstruction to triage and decision support. Clara is the base for clinical AI apps. → Holoscan: real-time intelligence for devices Think of surgery, endoscopy, ultrasound. Holoscan runs AI on the edge, next to the patient. Low latency, high throughput. Guidance, overlays, alerts, right when the clinician needs them. → BioNeMo: generative models for biology Here we switch from “test one molecule at a time” to “let AI search the space”. Proteins, RNA, small molecules, antibodies. BioNeMo lets teams simulate, design and rank options before they ever touch a lab bench. → MONAI: the open standard for medical imaging AI Framework for segmentation, detection, classification. Built with clinicians and researchers. If you train an imaging model today, chances are MONAI is in the stack. Put together, this looks less like a set of products. It looks more like: • a compute layer • a model and tool layer • a workflow and device layer All tuned for health. We move from “try in the clinic, then learn” to “simulate on digital twins, then translate”. Fewer failed trials. More personalized therapies. Faster loops between lab, clinic and home. As a health futurist, I see NVIDIA move to AI as infrastructure for prevention, diagnostics and treatment. I am curious how you think simulation-first medicine will change the way we design, test and deliver care. picture and source: NVIDIA

Seattle just dropped something serious into the biotech conversation, and it is not another lab coat trying to reinvent the pipette. Synthesize Bio has stepped out of stealth with a $10 million seed round, backed by Madrona Venture Group alongside AI2 Incubator, Sahsen Ventures, LLC, Inner Loop Capital, and Point Field Partners. This is not a hype cycle, it is a calculated bet on the idea that the future of #drugdiscovery will be as much code as #chemistry, and the code has a name: GEM-1. Co-founders Robert Bradley, PhD, and Jeff Leek, PhD, are not weekend hobbyists with a side hustle. Both hold endowed chairs at Fred Hutchinson Cancer Center, where they spent years watching the bottleneck of wet-lab data slow progress while RNA-seq #datasets piled up like unsorted vinyl in a basement. Instead of just complaining about it, they built a biological foundation model capable of simulating #geneexpression experiments in silico. The result is a platform that lets #researchers test hypotheses in hours, not months, and at a fraction of the cost of traditional experiments. GEM-1 is not smoke and mirrors. It is trained on one of the most rigorously curated RNA-seq datasets on the planet and is already available via web platform and API clients in R and Python. Researchers can plug it directly into their pipelines, run experiments that would be impossible in the lab, and get predictive data that maps against wet-lab results. The company has published a preprint on bioRxiv showing GEM-1's accuracy, and it has completed a #SOC2 audit to calm the compliance crowd. For a seed-stage startup, that is a signal of intent. The question is not whether #biopharma will use platforms like this, but how fast they will realize the economics demand it. Running thousands of experiments in the lab costs millions and burns years. Running them in silico with a generative genomics model means researchers can kill bad ideas early, double down on promising ones, and enter trials with higher confidence. That is not a nice-to-have; it is a competitive edge in an industry where every month matters and every failed trial costs fortunes. The $10 million will fuel development of next-gen GEM models, expand partnerships, and scale infrastructure. Rob Bradley and Jeff Leek are building more than a company; they are building a category that fuses deep learning with #biology at a moment when the genomics market is projected to surpass $50 billion by 2030. Investors like Madrona and AI2 Incubator are not betting small, they are placing chips on the table that #generativeAI in genomics is inevitable, and Synthesize Bio has the dataset, the science, and the credibility to make it real. #Startups #StartupFunding #VentureCapital #SeedRound #AI #GenAI #Biotech #Biotechnology #HealthTech #Healthcare #Data #DataDriven #Compliance #Infrastructure #Technology #Innovation #TechEcosystem #StartupEcosystem If software engineering peace of mind is what you crave, Vention is your zen.

BioEmu-1, or Biomolecular Emulator-1, is an **open-source** deep-learning model developed by Microsoft Research. It is designed to generate thousands of protein structures per hour, providing insights into the various conformations that proteins can adopt. COMPARISON WITH MOLECULAR DYNAMICS (MD) SIMULATIONS: 1️⃣ Faster and more efficient: BioEmu-1 can generate thousands of protein structures per hour on a single GPU 2️⃣ Leaner computational needs ➡️ Cost efficiency: BioEmu-1 uses about 0.001% of the GPU processing time required by standard MD simulations 3️⃣ Scalability: BioEmu-1's efficiency allows for large-scale studies of protein dynamics that were previously impractical due to resource constraints 4️⃣ Dynamic Insights: BioEmu-1 can simulate the dynamic behavior of proteins 5️⃣ Open-Source Accessibility: BioEmu-1 is available as an open-source tool APPLICATIONS IN RESEARCH ▪️ Drug Discovery: By simulating the dynamic behavior of proteins, BioEmu-1 helps researchers identify potential drug targets and design more effective drugs. ▪️ Protein Engineering: Researchers can use BioEmu-1 to design proteins with specific functions. This is particularly useful in developing enzymes for industrial processes or creating proteins with therapeutic properties ▪️ Disease Research: BioEmu-1 aids in studying diseases caused by protein misfolding or structural abnormalities such as Alzheimer's and Parkinson's ▪️ Structural Biology: The model provides a deeper understanding of protein structures and their functions, complementing experimental techniques like cryo-electron microscopy and X-ray crystallography ▪️ Biophysics: BioEmu-1 allows researchers to study the fundamental principles of protein dynamics and stability, contributing to our overall knowledge of molecular biology Learn more: https://lnkd.in/eeX4Cec7

What if the most important breakthrough in drug discovery isn’t a new molecule—but a living, learning digital twin of the human body? The challenge perhaps is not to design the next therapeutic but to shorten the path and odds from design, discovery and approvals by a near 100% bio-simulation of the human system. At the intersection of AI and quantum computing, we’re entering an era where biosimulation could: • Predict drug safety and efficacy before the first clinical trial • Model complex biological systems in ways no supercomputer alone could achieve • Personalize treatments by simulating how an individual’s biology will respond—long before they take the first dose We’re already seeing momentum: • NVIDIA BioNeMo is enabling large-scale biological language models to simulate protein interactions at atomic precision. • SandboxAQ is using quantum-inspired algorithms to accelerate drug discovery and molecular design. • Quantinuum is partnering with pharma to model complex molecular systems that classical computing struggles to handle. AI brings pattern recognition at unprecedented scale. Quantum offers the computational depth to simulate interactions and dynamics once thought impossible. Together, they could compress years of R&D into months, cut trial failures, and make precision medicine truly scalable. If we can model life this accurately—shouldn’t biosimulation be at the core of how we design and approve the next generation of therapies?