Improving antibody discovery using machine learning techniques
Official title: REPTOR: accelerating antibody discovery and improving hits with machine learning
Funded at Abterra Biosciences, INC. · NIH-11322291 · Data updated 2026-11-12
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.
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 | June 1, 2025 |
| Project end | July 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $63,564 |
| NIH project number | 3R44GM137688-02S1 |
| Project ID | NIH-11322291 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for participation or benefit from this research include individuals who have recovered from COVID-19 and have a strong immune response.
✕Less likely to help
Patients who are not recovering from COVID-19 or those without a significant immune response may not benefit from this research.
What this research studies
This research focuses on enhancing the discovery of antibody therapeutics by integrating advanced software that utilizes high-throughput sequencing data. By applying machine learning algorithms, the project aims to streamline the process of identifying effective antibodies from the immune responses of individuals, particularly in the context of the COVID-19 pandemic. The methodology involves analyzing large datasets to improve the success rate of antibody discovery and to refine existing antibody candidates. Patients may benefit from faster access to new and effective antibody treatments developed through this innovative approach.
Why it matters
Potential benefit: If successful, this research could lead to the rapid development of more effective antibody therapies for various diseases, including those related to COVID-19.
How similar studies have gone: Other research has shown promise in using high-throughput sequencing and machine learning for antibody discovery, indicating that this approach could be effective.
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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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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REPTOR: Using machine learning to find new antibody treatments
This project is creating new software to help scientists discover better antibody treatments for autoimmune diseases more quickly.
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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.