Over the next four years, PNNL and University of Arizona will develop open-source computational tools to better identify and characterize the viruses associated with the human microbiome.

Armed with some of the world’s most advanced instrumentation, researchers at PNNL are working to analyze huge amounts of data and uncover hidden biological connections.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

CACTUS provides AI-powered cheminformatics for autonomous science.

Scientists are using artificial intelligence and powerful computing to sculpt new molecules in an effort to treat disease.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

Scientists plumbed the depths of nearly 3,000 soil samples from around the globe to compile the heftiest atlas of soil viruses ever created.

Neeraj Kumar discusses how AI can transform scientific research at the Platform for Advanced Scientific Computing Conference and Trillion Parameter Consortium European Workshop.

PMedIC, the PNNL and OHSU joint research collaboration, contributes to student success and impact in biomedicine.

A compilation of soil viral genomes provides a comprehensive description of the soil virosphere, its potential to impact global biogeochemistry, and an open database for future investigations of soil viral ecology.

Data reveals mechanisms in our bodies that can help us fight viral pathogenesis.

New apps help researchers with statistical analyses of data.

PNNL researchers are working to provide the technical assistance and expertise needed for communities to shape their clean energy future.

Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.

Scientists have learned more about how drug resistance develops in patients with acute myeloid leukemia and how the process might be slowed.

Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.

The new PTM-Psi workflow opens the door for uncovering molecular insights within proteomics datasets.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.