Computational tools to boost biomolecular NMR for disease research
Official title: Computational and Data Science Approaches to Enhance Biomolecular NMR
Funded at University of Connecticut Sch of Med/dnt · NIH-11392527 · Data updated 2026-11-12
A research project will create and improve computer tools that help scientists use biomolecular NMR to study diseases.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Connecticut Sch of Med/dnt NIH-funded |
| Lab location | 1 site (Farmington, United States) |
| Project start | September 1, 2025 |
| Project end | July 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $457,875 |
| NIH project number | 5R35GM158031-02 |
| Project ID | NIH-11392527 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with diseases where biomolecular NMR can reveal molecular interactions and biomarkers may indirectly benefit through faster translation of discoveries.
✕Less likely to help
Patients whose disease biology is not studied by NMR or that do not participate in related clinical studies may not benefit.
What this research studies
This project will develop user-friendly computational methods and workflows to make biomolecular NMR more sensitive, higher-resolution, and easier to reproduce. It will integrate with the Biological Magnetic Resonance Data Bank and the NMRbox platform to share data and software. The focus is on studying biomolecules involved in disease and organizing complex data from mixtures to support biomarker discovery and drug screening. Researchers, clinicians, and data scientists will be able to apply these tools to analyze NMR results more efficiently and reproducibly.
Why it matters
Potential benefit: If successful, patients could benefit from faster biomarker discovery and improved drug development informed by clearer molecular insights.
How similar studies have gone: Previous work shows that integrating NMR data with shared data platforms improves analysis speed and data reuse; this project expands those capabilities.
Where this research is happening
-
University of Connecticut Sch of Med/dnt
Farmington, United States Active
Researchers
- Principal investigator: Hoch, Jeffrey C — University of Connecticut Sch of Med/dnt
- Study coordinator: Hoch, Jeffrey C
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.