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

Q-Chem是一种最先进的商业计算量子化学 已帮助约60,000名用户建模的软件程序 广泛学科的分子过程，包括生物、化学、 和材料科学。 在这个建议中，我们寻求显著减少计算时间(现在 约500,000个CPU小时)才能获得准确的自由能分布 酶促反应。具体地说，我们建议使用多时间步长(MTS) 模拟方法，其中低水平(和不太精确)的量子化学或 机器学习模型用于在以下位置传播系统(即移动所有原子 每个时间步长(通常为0.5或1 fS)，然后是高级别(即更精确和 昂贵的)量子化学方法被用来修正原子上的力 以更长的时间间隔。通过这种方式，可以在最高端执行模拟。 与执行的模拟相比，只需很短的时间就能得到能级表面 只使用高水平的量子化学方法。 在第一阶段的方案中，我们成功地重新参数化了低能级量子 化学模型和开发的用于MTS模拟的机器学习模型。 通过这些发展，我们能够延长高级别部队的更新 设置为每8 fS或更长时间一次。在第二阶段，我们将进一步完善 并使开发低成本模型的工作流程自动化，这将进一步 提高我们的MTS模拟的计算效率。此外，这些 将结合EnzyDock方法的进展，以促进对 多步酶反应与共价/非共价抑制剂的设计 和突变的酶。 这些新工具的加入也将进一步加强Q-Chem在 分子建模软件市场的全球领先者，使我们的程序 最高效、最可靠的计算量子化学软件包 模拟大型、复杂的化学/生物系统。

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.