NVIDIA Healthcare

Grateful to @NVIDIAAI for early access to Nemotron 3 Nano Omni. Healthcare data is everywhere; rarely connected. Exploring Agentic multimodal AI to unify text, audio, images & video for India-scale care. Read More: bit.ly/4cNRvUB #EkaCare #NVIDIA #NemotronOmni

(3/3) Try KERMT today nvda.ws/4mWsZDY Check out the challenge 👇 nvda.ws/4eiyURv

(2/3) Why KERMT? ✅ Pretraining: Uses Kinetic GROVER for rich structural/chemical embeddings ✅ Efficiency: Streamlined for NVIDIA-accelerated multi-task learning ✅ Reproducibility: Fully open-source and ready for your next lead-op pipeline

(1/3) 🛠️ We designed KERMT to bridge the gap between self-supervised graph learning and industrial ADMET workflows. Recent results from the OpenADMET-ExpansionRX challenge confirm the impact: 5 of the top 20 models used KERMT as their backbone to predict critical pharmacokinetic endpoints.

Scaling laws are powering AI. It’s time to scale biology. Today we’re launching the Virtual Biology Initiative to generate the data to unlock scaling laws in biology and build accurate predictive models of the cell. Digital representations of proteins are already expanding our understanding of life at the molecular level, and accelerating the design of molecules and medicines. Accurate digital representations of the cell could reveal the mechanisms that are responsible for disease, and show how to reverse them. The protein data bank, and worldwide repositories of protein sequence biodiversity were created through decades of work by the scientific community. The advances in artificial intelligence for proteins would not have been possible without them. The cell is orders of magnitude more complex, and we will need to create the data in just a few years rather than decades. This will require a coordinated global effort. We're partnering with Broad, Wellcome Sanger, Arc, Allen, Human Cell Atlas, Human Protein Atlas, NVIDIA, and Renaissance Philanthropy. Biohub is contributing to this effort as both a funder and a builder. We are developing microscopy to observe millions of cells in living organisms, and cryo-ET to resolve the cell in atomic detail. We're building instruments that expand the range of modalities and parameters that can be simultaneously measured. We’re developing molecular, cellular, and tissue engineering to create models of disease and design interventions. The data we generate will be available to the worldwide scientific community. We’re also committing $100M over the next five years to support work beyond Biohub. We invite other scientific teams and funders to join. Link: biohub.org/news/virtual-b…

(2/2) You can now run Boltz model structure predictions on tens of thousands of tokens (e.g., >20k) by scaling across many GPUs - overcoming single-device memory limits without approximations or chunking. Full write-up, including distributed implementations of triangle operations 👉 nvda.ws/4tFWuN5

(1/2) Struggling with the memory limits of structure prediction for large biomolecular systems? ✂️ Common workarounds, like cropping sequences or chunking inputs, can break global interactions and bias predictions by removing long-range context. Our latest tech blog explains how the NVIDIA BioNeMo team implemented Context Parallelism (FoldCP): distributing a single large molecular system across multiple GPUs, rather than just increasing batch size. 🧵⬇️

(2/2) 🔗 nvda.ws/4mFl7GO (describing the GFMBench‑API framework) 💻 nvda.ws/4t6FtdT (reference implementation) 🔬Example study using GFMBench‑API ⬇️ nvda.ws/4eApelm

(1/2) 🧬 The evaluation of genomic foundation models is scattered - GFMBench‑API brings it together. 🚀 GFMBench-API solves this by providing a universal middleware with a standardized API that unifies evaluation - covering tasks such as regulatory element prediction and variant effect scoring - to enable transparent, scalable comparisons. 🧵👇

NVIDIA CEO Jensen Huang joined the world's leading scientists, technologists, and innovators at the 12th @brkthroughprize ceremony. On the red carpet, he shared why science is a team sport, and what it means to celebrate the people pushing humanity forward. 🔭

Introducing GPT-Rosalind, our frontier reasoning model built to support research across biology, drug discovery, and translational medicine.

What if a blood test could detect Alzheimer's before symptoms appear? 🧠 NVIDIA Inception member Prima Mente built the world's first whole-genome epigenetic AI foundation model, achieving up to 97% accuracy in early detection. 🧬 🔗 nvda.ws/4kdSFKM

📖 Read the launch details nvda.ws/4mv4vBA 💻 Test the model nvda.ws/4ms9qmA

🧬 Deploying genomic AI just got easier The NVIDIA Evo 2 NIM microservice is now live on Amazon SageMaker AI. If you're building in bioinformatics, you can instantly spin up this foundation model to map DNA sequences to protein functions - all with zero infrastructure overhead on AWS's fully managed environment. 🧵 ⬇️

📖 Blog nvda.ws/47TPIud 💻 Code 👇 nvda.ws/4tJjxWV

⚡ Accelerate your molecular clustering with nvMolKit and NVIDIA GPUs Our new guest post on Greg Landrum's RDKit blog breaks down how to achieve over 200x speedups on a local RTX 5080, and well over 1,000x on datacenter GPUs. Read the full breakdown and grab the code 🧵

@emblebi @GoogleDeepMind @SeoulNatlUni 🆓 The dataset is free for anyone to explore, download, and integrate into downstream analyses. Dive in today and start uncovering protein‑interaction secrets ⬇️ nvda.ws/4tFuskk

This update: ▪️Democratizes access to high‑compute biological insights by providing freely downloadable, high‑accuracy complex predictions ▪️Shifts the focus from isolated monomers to biologically relevant molecular interactions, enabling systematic mapping of cellular machinery ▪️Scales open‑science workflows through NVIDIA‑accelerated tooling (e.g., GPU‑enabled inference pipelines and cloud‑ready APIs)

🚀 The largest-ever open‑source protein‑complex treasure trove - 1.7 million of AI‑predicted complexes now live in the AlphaFold Database In collaboration with @emblebi, @GoogleDeepMind, and @SeoulNatlUni, we have added millions of predicted complexes to the AlphaFold Database to accelerate global health research. 🧵👇

(2/2) Get started with nvQSP on GitHub: github.com/NVIDIA-Digital…

(1/2) Introducing nvQSP: The "Flight Simulator" for medicine, turning weeks of simulation time into hours. nvQSP allows researchers to simulate biological variability across thousands of "virtual patients" to forecast drug safety and efficacy before human trials. - 77x Speedup: Accelerates drug simulations from 350 ms to 4.5 ms per patient. - High Throughput: Processes: ~220 virtual patients per second on H200. - Bit-Exact Precision: Uses full FP64 for the rigor required in FDA/regulatory filings. github link 👇