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.