SBIR-STTR Award

Q-Chem is a state-of-the-art commercial computational quantum chemistry 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 method 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, our goal is to allow the high-level force update only once every 40—50 fs by identifying appropriate lower-level theories (Aim 1) and incorporating machine-learning techniques (Aim 2). This will accelerate accurate free energy simulations by 20—25 fold, reducing the overall computer time to around 25,000 CPU hours. Thus, our new MTS simulation method will make it feasible to routinely perform computational studies on enzymatic reaction mechanism. 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.

Public Health Relevance Statement:
In this project, we seek to significantly reduce the computational time (ca. 500,000 CPU hours) required to obtain accurate free energy profiles of enzymatic reactions to ca. 25,000 CPU Hours. Building upon sophisticated quantum mechanics, this can lead to reliable and quick predictions of enzyme activities.

Project Terms:
Acceleration; Accounting; Adopted; Back; Biochemical; Biochemical Reaction; biological systems; Biology; Biomedical Research; Chemicals; Chemistry; Communities; Complex; Computer Simulation; Computer software; computer studies; Computers; cost; density; Development; Discipline; enzyme activity; enzyme model; Enzymes; Foundations; Free Energy; Goals; Hour; Hybrids; improved; innovation; Lead; learning strategy; Machine Learning; Maps; materials science; Mechanics; Methodology; Methods; Modeling; Molecular Conformation; Molecular Machines; molecular mechanics; molecular modeling; Pathway interactions; Performance; Phase; Positioning Attribute; Potential Energy; Process; programs; Protein Conformation; Proteins; quantum; quantum chemistry; quantum computing; Quantum Mechanics; Reaction; Recipe; Research; Research Personnel; Sampling; Scheme; simulation; Solvents; Surface; System; Techniques; theories; Time; time interval; tool; Update

Q-Chem is a state-of-the-art commercial computational quantum chemistrysoftware program that has aided about 60,000 users in their modeling ofmolecular processes in a wide range of disciplines, including biology, chemistry,and materials science.In this proposal, we seek to significantly reduce the computational time (nowaround 500,000 CPU hours) required to obtain accurate free energy profiles ofenzymatic reactions. Specifically, we propose to use a multiple time step (MTS)simulation method, where a low-level (and less accurate) quantum chemistry ormachine learning model is used to propagate the system (i.e. move all atoms) ateach time step (usually 0.5 or 1 fs), and then a high-level (i.e. more accurate andexpensive) quantum chemistry method is used to correct the force on the atomsat 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 performedonly using the high-level quantum chemical method.In the Phase I proposal, we successfully re-parameterized low-level quantumchemistry models and developed machine learning models for MTS simulations.Through these developments, we were able to extend the high-level force updateto only once every 8 fs or longer. In the Phase II period, we will further improveand automate the workflow for developing the low-cost models, which will furtherenhance the computational efficiency of our MTS simulations. In addition, theseadvances will be combined by the EnzyDock method to facilitate the study ofmulti-step enzyme reactions and the design of covalent/noncovalent inhibitorsand mutant enzymes.The addition of these new tools will also further strengthen Q-Chem's position asa global leader in the molecular modeling software market, making our programthe most efficient and reliable computational quantum chemistry package forsimulating large, complex chemical/biological systems.

Public Health Relevance Statement:
In this project, we seek to significantly reduce the computational time (ca. 500,000 CPU hours) required to obtain accurate free energy profiles of enzymatic reactions to ca. 25,000 CPU Hours. Building upon sophisticated quantum mechanics, this can lead to reliable and quick predictions of enzyme activities and design of inhibitors and mutant enzymes.

Project Terms: