# Data Mining and Machine Learning Guided QM/MM and QM-Cluster Modeling of Enzymatic Reactions

> **NIH NIH R35** · UNIVERSITY OF MEMPHIS · 2024 · $327,397

## Abstract

Project Summary/Abstract
Computational modeling methods have been widely applied in protein structure prediction, drug
discovery and enzyme bioengineering to provide atomic-level insight into enzymatic reactions and
functions. Accuracy and efficiency are the two goals that motivate the development of new
methods in this field. However, the methodological best practices are still lacking in achieving high
throughput and accuracy. In quantum mechanics/molecular mechanics (QM/MM) and QM-cluster
enzyme modeling, series of decisions such as molecule partitioning into QM and MM regions,
protonation states of residues, and computational setting rely on good understanding of the
problem and knowledge of the enzyme as well as available computational methods. In this
proposed project, machine learning methods will be applied in computational enzyme modeling
for a better and more systematic solution. The proposed project is innovative as it combines a)
data mining and machine learning on published experimental and computational works which will
efficiently and systematically collect knowledge for research; b) machine learning methods can
weigh different components of computational modeling and make optimal decisions automatically;
c) the results of this work will provide a rational strategy for accurate and efficient QM/MM and
QM-cluster simulations in future studies of different protein systems, drug design and even other
scientific research domains. The proposed project will focus on two enzyme systems that will
serve as case studies: a) Chorismate Mutase which is a potential target for designing antibiotics
and b) the Cytochrome P450 superfamily of metalloenzymes which are largely involved in drug
metabolism via various reaction mechanisms.

## Key facts

- **NIH application ID:** 10877895
- **Project number:** 5R35GM145206-03
- **Recipient organization:** UNIVERSITY OF MEMPHIS
- **Principal Investigator:** Qianyi Cheng
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $327,397
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877895

## Citation

> US National Institutes of Health, RePORTER application 10877895, Data Mining and Machine Learning Guided QM/MM and QM-Cluster Modeling of Enzymatic Reactions (5R35GM145206-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10877895. Licensed CC0.

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