# Multiscale ab initio QM/MM and Machine Learning Methods for Accelerated Free Energy Simulations

> **NIH NIH R44** · Q-CHEM, INC. · 2023 · $677,985

## Abstract

Q-Chem is a state-of-the-art commercial computational quantum chemistry
software program that has aided about 60,000 users in their modeling of
molecular processes in a wide range of disciplines, including biology, chemistry,
and materials science.
In this proposal, we seek to significantly reduce the computational time (now
around 500,000 CPU hours) required to obtain accurate free energy profiles of
enzymatic reactions. Specifically, we propose to use a multiple time step (MTS)
simulation method, where a low-level (and less accurate) quantum chemistry or
machine learning model is used to propagate the system (i.e. move all atoms) at
each time step (usually 0.5 or 1 fs), and then a high-level (i.e. more accurate and
expensive) quantum chemistry method is used to correct the force on the atoms
at longer time intervals. In this way, the simulation can be performed at the high-
level energy surface in a fraction of time, compared with simulations performed
only using the high-level quantum chemical method.
In the Phase I proposal, we successfully re-parameterized low-level quantum
chemistry models and developed machine learning models for MTS simulations.
Through these developments, we were able to extend the high-level force update
to only once every 8 fs or longer. In the Phase II period, we will further improve
and automate the workflow for developing the low-cost models, which will further
enhance the computational efficiency of our MTS simulations. In addition, these
advances will be combined by the EnzyDock method to facilitate the study of
multi-step enzyme reactions and the design of covalent/noncovalent inhibitors
and mutant enzymes.
The addition of these new tools will also further strengthen Q-Chem's position as
a global leader in the molecular modeling software market, making our program
the most efficient and reliable computational quantum chemistry package for
simulating large, complex chemical/biological systems.

## Key facts

- **NIH application ID:** 10696727
- **Project number:** 2R44GM133270-02A1
- **Recipient organization:** Q-CHEM, INC.
- **Principal Investigator:** Xintian Feng
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $677,985
- **Award type:** 2
- **Project period:** 2019-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10696727

## Citation

> US National Institutes of Health, RePORTER application 10696727, Multiscale ab initio QM/MM and Machine Learning Methods for Accelerated Free Energy Simulations (2R44GM133270-02A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10696727. Licensed CC0.

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