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.

Advanced proteomic analysis tools dig deep into human blood plasma to understand the molecular nature of advanced alcoholic disease.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

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

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.

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.

Proteomics revealed molecular mechanisms by which the drug Verapamil could prolong beta cell function in early-onset type 1 diabetes patients.

Study highlights microbiome’s role in regulating xenobiotic metabolism.

Development of a one-meter path length miniature ion mobility spectrometer based on multilevel structures for lossless ion manipulations.

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

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

Soil moisture influences both the activity of soil DNA viruses, as well as the composition and abundance of RNA viruses.

Knowing which bacteria in a community are involved with carbon cycling could help scientists predict how microbial carbon storage and release could influence future climate dynamics.

New quantum chemistry pipeline can help predict the effects of bioengineering.

High-throughput biochemical assays targeting a vital viral protein identified one molecule out of more than 13,000 with promising antiviral activity against SARS-CoV-2.