Gosline works to develop computational algorithms that are uniquely targeted for rare disease work by doing foundational research in model system development. This work can be expanded to all model systems in human disease.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

A paper by PNNL researchers has been chosen as one of 32 NIEHS Papers of the Year—a top 1% honor in the research field.

Findings show method to better prepare human lung tissue for study; it represents the latest mass spectrometry-based omics advances.

New research findings published in Science Advances (November 2022), help explain the progression of Alzheimer-related dementia in each patient. The findings outline a biological classification system that predicts disease severity.

Silicone wristbands could be more convenient option for collecting exposure data.

We are pleased to welcome 20 new joint appointees to PNNL.

A process developed at PNNL that converts biomass and waste into a chemical intermediate or into gasoline, diesel, and jet fuel is available for commercial licensing.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

PNNL scientists have developed a new mass spectrometry method for in-depth analysis of proteins in individual cells.

Data from increasingly complex mass spectrometry instruments met with adaptable data analysis solution.

Mowei Zhou, a chemist with the Environmental Molecular Sciences Laboratory, is speaking at the ACS spring conference on his latest protein discoveries for a plant that could transform biofuels production.

The scientist behind the online cartoon RedPen/BlackPen explains how humor can build bridges between scientists and the public.

PNNL scientists are protecting first responders and others by expanding what’s known about fentanyl, the driver of the opioid epidemic.

Researchers characterize protein signatures that indicate Acute Kidney Injury in COVID-19 patients.

Simulations reveal advantages of using new model vs. allometric scaling for risk assessment.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

Scientists have devised a way to engineer yeast to produce sustainable, eco-friendly commodity chemicals using computing power as a guide.

The nested nanoPOTS chip is the next generation of technology developed at PNNL to prepare single cells for proteomics.

Researchers map nearly 9,000 proteins in the human lungs, providing a clearer picture of development.