REPTOR: Using machine learning to find new antibody treatments
Official title: REPTOR: accelerating antibody discovery and improving hits with machine learning
Funded at Abterra Biosciences, INC. · NIH-11194970 · Data updated 2026-11-12
This project is creating new software to help scientists discover better antibody treatments for autoimmune diseases more quickly.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Abterra Biosciences, INC. NIH-funded |
| Lab location | 1 site (San Diego, United States) |
| Project start | April 1, 2020 |
| Project end | July 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $961,244 |
| NIH project number | 5R44GM137688-03 |
| Project ID | NIH-11194970 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with autoimmune diseases or other conditions that could be treated with antibody therapies might indirectly benefit from this research.
✕Less likely to help
Patients whose conditions are not treated with antibody therapies would likely not see a direct benefit from this specific software development.
What this research studies
Current methods for finding new antibody treatments are often slow and may miss important options. This project aims to develop advanced software that uses high-throughput sequencing to improve how we find these antibodies. The software will make it easier to incorporate detailed genetic information into existing discovery methods, speeding up the process and increasing the chances of finding effective treatments. It will also help improve existing antibody candidates by learning from the body's natural immune responses.
Why it matters
Potential benefit: If successful, this work could lead to faster development of more effective antibody therapies for patients with autoimmune diseases and other conditions.
How similar studies have gone: While traditional antibody discovery methods are decades old, newer single-cell approaches are gaining traction, and this project aims to integrate large-scale data in a novel way that has previously been challenging.
Where this research is happening
-
Abterra Biosciences, INC.
San Diego, United States Active
Researchers
- Principal investigator: Castellana, Natalie — Abterra Biosciences, INC.
- Study coordinator: Castellana, Natalie
Related research
Other NIH-funded projects on the same topics.
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Improving antibody discovery using machine learning techniques
This study is working on using smart computer programs to help find better antibody treatments for COVID-19 faster, so patients can get access to new and effective options more quickly.
-
A new antibody profiling platform to help diagnose autoimmune diseases like lupus
A new method will analyze many antibody sequences in blood to aid diagnosis of autoimmune diseases such as lupus.
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Improving the discovery of antibody treatments using machine learning
This study is working on a new way to find better antibody treatments for diseases like autoimmune conditions and COVID-19, using smart software to speed up the process, so patients can get effective treatments more quickly.
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.