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@NVIDIAHealth "@Merck Check out the open-source accelerated implementation today ➡ Read the paper ➡"
X Link @NVIDIAHealth 2025-10-16T14:41Z 11.9K followers, XXX engagements

"🧑⚕ Nurses around the world are stretched thin. 📋 More paperwork less patient time. 🤖 Nurabot a nursing robot developed by @HonHai_Foxconn and Kawasaki Heavy Industries is leveraging #PhysicalAI to reduce nurses workloads by up to 30%. Powered by #NVIDIAIsaac #NVIDIAHoloscan and #NVIDIAJetson Orin Nurabot helps offload time-consuming fatiguing tasks to give nurses more time to focus on patient care. #COMPUTEX2025"
X Link @NVIDIAHealth 2025-05-20T23:15Z 11.9K followers, 2569 engagements

"🔬 Fold proteomes privately no queues no bottlenecks Bring protein structure prediction to your desktop. Powered by the GB10 Grace Blackwell Superchip our new NVIDIA DGX Spark will deliver: ⚡ X petaFLOP of AI performance 🧠 XXX GB coherent unified system memory Its more than a workstation - its a personal AI supercomputer for digital biology. Get ready to #SparkSomethingBig"
X Link @NVIDIAHealth 2025-09-22T22:30Z 11.9K followers, 13.2K engagements

""The age of AI has begun. And it's transforming every industry including medicine. The Cancer AI Alliance is pioneering a new approach: federated learning allowing researchers to collaborate without sharing data. CAIA is connecting cancer centers accelerating breakthroughs and shaping a future where AI improves care for every patient. Together." - Jensen Huang Founder and CEO of NVIDIA"
X Link @NVIDIAHealth 2025-10-01T18:18Z 11.9K followers, 73.1K engagements

"Pretraining graph transformers works for ADMET and the advantage increases with more data and tasks. 📊 In collaboration with @Merck KERMT is statistically best in 18/30 endpoints on Mercks internal dataset. Efficient reimplementation is 2.2x faster for fine-tuning and 2.9x faster for inference showing near-linear distributed pretraining efficiency across multiple GPUs"
X Link @NVIDIAHealth 2025-10-16T13:00Z 11.9K followers, 3396 engagements

"What is AvgFlow It is a model training and inference framework for accelerating flow-based models that generate 3D molecular conformers. Uses SO(3)-Averaged Flow to accelerate model training (fewer epochs needed and eliminating the need of rotation augmentation) and reflow + distillation to accelerate model inference (fewer inference steps) yielding faster convergence and better performance across architectures while maintaining accuracy. 🔗"
X Link @NVIDIAHealth 2025-10-16T16:00Z 11.9K followers, 8527 engagements