Predicting peptide drug binding with a fast, quantum-accurate AI tool
Official title: Binding free energy predictions for peptide drugs: A novel machine learning-based peptide simulation model for high-throughput computational lead optimization with quantum mechanical accuracy.
Funded at Quantum Simulation Technologies, INC. · NIH-11408484 · Data updated 2026-11-12
A new AI-powered simulation platform aims to reliably predict how peptide drugs bind to targets, helping researchers design better therapies.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Quantum Simulation Technologies, INC. NIH-funded |
| Lab location | 1 site (Boston, UNITED STATES) |
| Project start | January 1, 2024 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $1,022,908 |
| NIH project number | 2R44GM152994-02 |
| Project ID | NIH-11408484 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not a clinical trial; however, researchers and patients may benefit indirectly from faster, more reliable peptide drug design.
✕Less likely to help
Direct patient participation is not required; this is a computational development grant.
What this research studies
Peptide drugs can fight difficult disease targets, but their flexible shapes make accurate prediction hard. This grant funds a second-generation, machine-learning force field combined with a GPU-accelerated quantum-molecular mechanics engine to improve sampling and energetics for peptides. The goal is a transformative, first-principles pipeline that can rapidly predict binding affinity and important drug properties, speeding lead optimization. The project builds on prior work and focuses on extending parameter coverage to new chemistries and improving convergence of critical calculations.
Why it matters
Potential benefit: Could accelerate development of peptide therapies with higher accuracy and faster lead optimization.
How similar studies have gone: Prior Phase I work on QuaiPa informed this Phase II/III effort, with a track record suggesting potential for improved predictive capability.
Where this research is happening
-
Quantum Simulation Technologies, INC.
Boston, United States Active
Researchers
- Principal investigator: Pearlman, David a — Quantum Simulation Technologies, INC.
- Study coordinator: Pearlman, David a
Related research
Other NIH-funded projects on the same topics.
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Improving drug discovery using advanced computational methods.
This study is looking at new ways to find better medications faster by using advanced computer techniques to see how well different drug-like molecules connect with their targets, which could help patients get more effective treatments sooner.
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.