Designing new antibodies using advanced computer models
Generative neural networks for structure-based antibody design
This study is working on making new antibodies to help fight COVID-19 and other illnesses faster and cheaper, so patients can get better tests and treatments that are specially designed for their needs.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-10929333 on NIH RePORTER |
What this research studies
This research focuses on creating new antibodies tailored for specific medical needs, particularly in response to the COVID-19 virus. By utilizing generative neural networks, the researchers aim to streamline the process of antibody design, making it faster and more cost-effective. Patients may benefit from the development of more effective diagnostic tests and therapies that are specifically engineered to target their conditions. The approach involves sophisticated computational modeling to predict how antibodies will interact with viruses and other pathogens.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals affected by COVID-19 or those requiring targeted antibody therapies for other conditions.
Not a fit: Patients who do not have conditions requiring antibody treatments or those not affected by COVID-19 may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to the rapid development of highly specific antibodies that improve diagnostics and treatment options for patients.
How similar studies have performed: Previous research has shown promise in using computational methods for antibody design, indicating a potential for success with this novel approach.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Huang, Possu — Stanford University
- Study coordinator: Huang, Possu
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 below.