Using machine learning to predict how antibodies interact with antigens
Official title: Machine learning for identifying antigen-antibody interactions from massive sequencing data
Funded at Ut Southwestern Medical Center · NIH-11105726 · Data updated 2026-11-12
This study is exploring new ways to use computer technology to better understand how antibodies and antigens work together, which could help find new treatments and tests for immune-related diseases, making things easier and faster for patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ut Southwestern Medical Center NIH-funded |
| Lab location | 1 site (Dallas, United States) |
| Project start | March 1, 2025 |
| Project end | February 28, 2030 |
| Funding year | 2025 |
| Award (this year) | $667,013 |
| NIH project number | 1R01AI190103-01 |
| Project ID | NIH-11105726 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with immune-related diseases who may benefit from improved diagnostic methods or new antibody therapies.
✕Less likely to help
Patients without immune-related conditions or those who do not respond to antibody therapies may not benefit from this research.
What this research studies
This research focuses on utilizing advanced machine learning techniques to analyze large sequencing datasets in order to predict interactions between antibodies and antigens. By leveraging high-throughput sequencing technologies and innovative protein structure prediction models, the project aims to develop accurate and efficient methods for identifying these interactions. This could lead to the discovery of new therapeutic antibodies and the creation of diagnostic tools for immune-related diseases, ultimately improving patient care. The approach is designed to overcome the limitations of traditional experimental methods, which are often slow and expensive.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate diagnostic tools and new therapies for immune-related diseases.
How similar studies have gone: Previous research has shown promise in using machine learning for similar predictive tasks, indicating a potential for success in this novel application.
Where this research is happening
-
Ut Southwestern Medical Center
Dallas, United States Active
Researchers
- Principal investigator: Wang, Tao — Ut Southwestern Medical Center
- Study coordinator: Wang, Tao
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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.