Data-driven QM/MM and QM-Cluster Modeling for Enzyme Reactions
Official title: Data Mining and Machine Learning Guided QM/MM and QM-Cluster Modeling of Enzymatic Reactions
Funded at University of Memphis · NIH-11389619 · Data updated 2026-11-12
A data-guided approach will use machine learning to make QM/MM and QM-cluster modeling of enzymes faster and more accurate, helping us understand how enzymes work and aiding future drug design.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Memphis NIH-funded |
| Lab location | 1 site (Memphis, United States) |
| Project start | September 1, 2022 |
| Project end | July 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $327,397 |
| NIH project number | 5R35GM145206-05 |
| Project ID | NIH-11389619 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not applicable for this grant as described (computational modeling study).
✕Less likely to help
No direct patient participation or benefit from this project as described.
What this research studies
This project combines machine learning with quantum mechanics/molecular mechanics modeling to improve how we simulate enzymatic reactions. Researchers will mine published data to train models that guide decisions about system partitioning, protonation states, and computational settings. The goal is to deliver a rational, automated strategy that accelerates accurate simulations across different enzymes and protein systems. The work aims to benefit fields like drug discovery and bioengineering by making enzyme modeling more reliable and efficient.
Why it matters
Potential benefit: If successful, patients could benefit from faster and more accurate enzyme studies that support better drug design and enzyme engineering.
How similar studies have gone: Previous studies show that data-driven modeling can improve computational chemistry predictions, but this project seeks to generalize and automate the approach for enzymes.
Where this research is happening
-
University of Memphis
Memphis, United States Active
Researchers
- Principal investigator: Cheng, Qianyi — University of Memphis
- Study coordinator: Cheng, Qianyi
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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.