Predicting antibody sequences to fight new viruses quickly
Official title: Rapid response for pandemics: single cell sequencing and deep learning to predict antibody sequences against an emerging antigen
Funded at Keck Graduate Inst of Applied Life Scis · NIH-10845715 · Data updated 2026-11-12
This study is working on a new way to quickly find antibodies that can fight off new viruses, which could help patients get faster and better treatments for viral infections.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Keck Graduate Inst of Applied Life Scis NIH-funded |
| Lab location | 1 site (Claremont, United States) |
| Project start | September 16, 2021 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $1,219,945 |
| NIH project number | 3R01AI169543-01S1 |
| Project ID | NIH-10845715 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals at risk of or infected with emerging viral infections.
✕Less likely to help
Patients with established viral infections that do not require new antibody treatments may not benefit from this research.
What this research studies
This research focuses on developing advanced computational methods to predict antibody sequences that can effectively bind to new viral proteins. By utilizing deep learning techniques and analyzing structural features of antigens, the project aims to generate potential antibody candidates within 24 hours of identifying a new virus. Patients may benefit from faster and more effective treatments for emerging viral infections as this technology could lead to the rapid development of targeted therapies. The research involves complex modeling and data analysis to enhance our understanding of immune responses.
Why it matters
Potential benefit: If successful, this research could lead to the rapid development of effective antibody treatments for new viral infections, improving patient outcomes during pandemics.
How similar studies have gone: While the approach of using deep learning for antibody prediction is innovative, similar methodologies have shown promise in other areas of immunology, suggesting potential for success.
Where this research is happening
-
Keck Graduate Inst of Applied Life Scis
Claremont, United States Active
Researchers
- Principal investigator: Ray, Animesh — Keck Graduate Inst of Applied Life Scis
- Study coordinator: Ray, Animesh
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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.