Developing new peptide catalysts for selective chemical bond formation
Official title: Machine Learning and Automated Flow Synthesis for the Development of Peptide Catalysts for the Stereo- and Site-Selective Formation of C–C Bonds
Funded at Massachusetts Institute of Technology · NIH-11071160 · Data updated 2026-11-12
This study is exploring new ways to create special proteins that help make important building blocks for medicines, which could lead to better and faster drug development for patients like you.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts Institute of Technology NIH-funded |
| Lab location | 1 site (Cambridge, United States) |
| Project start | January 15, 2025 |
| Project end | January 14, 2028 |
| Funding year | 2025 |
| Award (this year) | $74,632 |
| NIH project number | 1F32GM157940-01 |
| Project ID | NIH-11071160 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for benefiting from this research are individuals with conditions that require novel therapeutic agents or improved drug formulations.
✕Less likely to help
Patients with conditions that are not addressed by new drug development or those who do not require peptide-based therapies may not benefit from this research.
What this research studies
This research focuses on creating innovative peptide catalysts that can selectively form carbon-carbon bonds, which is crucial for organic synthesis and drug development. By utilizing automated fast-flow peptide synthesis and machine learning, the project aims to design peptides with unique sequences and non-natural residues that enhance their reactivity. This approach seeks to bridge the gap between traditional catalysts and novel synthetic methods, potentially leading to more efficient pharmaceutical production. Patients may benefit indirectly through advancements in drug synthesis and development.
Why it matters
Potential benefit: If successful, this research could lead to more efficient and targeted drug development, improving treatment options for various diseases.
How similar studies have gone: Other research has shown promise in using machine learning and biocatalysts for drug synthesis, indicating a potential for success in this innovative approach.
Where this research is happening
-
Massachusetts Institute of Technology
Cambridge, United States Active
Researchers
- Principal investigator: Yu, Isaac Fu-Ray — Massachusetts Institute of Technology
- Study coordinator: Yu, Isaac Fu-Ray
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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.