Using machine learning to improve enzyme modeling for drug design
Official title: Data Mining and Machine Learning Guided QM/MM and QM-Cluster Modeling of Enzymatic Reactions
Funded at University of Memphis · NIH-10877895 · Data updated 2026-11-12
This study is exploring how advanced computer techniques can improve our understanding of enzymes, which are important for developing new medicines, making it easier and faster to predict how these enzymes work with potential drugs.
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 | 2024 |
| Award (this year) | $327,397 |
| NIH project number | 5R35GM145206-03 |
| Project ID | NIH-10877895 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with conditions that involve enzymatic reactions, particularly those related to drug metabolism or enzyme deficiencies.
✕Less likely to help
Patients with conditions unrelated to enzymatic functions or those not requiring drug interventions may not benefit from this research.
What this research studies
This research investigates how machine learning can enhance computational models used to understand enzymatic reactions, which are crucial for drug discovery. By integrating data mining techniques with quantum mechanics and molecular mechanics, the project aims to create more accurate and efficient models of enzymes. This approach will help automate decision-making in the modeling process, leading to better predictions of how enzymes function and interact with potential drugs. Ultimately, the goal is to streamline the development of new therapies by providing deeper insights into enzyme behavior.
Why it matters
Potential benefit: If successful, this research could lead to more effective drug design and development processes, potentially resulting in better treatments for various diseases.
How similar studies have gone: Previous research has shown promise in using machine learning for computational modeling in biochemistry, indicating that this approach could yield significant advancements.
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.