Journal ArticleDOI
Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.
Xiaoliang Pan,Junjie Yang,Richard Van,Evgeny Epifanovsky,Junming Ho,Jing Huang,Jingzhi Pu,Ye Mei,Ye Mei,Ye Mei,Kwangho Nam,Yihan Shao +11 more
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TLDR
A protocol for performing machine learning assisted free energy simulation of solution-phase and enzyme reactions at an ab initio quantum mechanical and molecular mechanical (ai-QM/MM) level of accuracy is reported.Abstract:
Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol-1 A-1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost.read more
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Journal ArticleDOI
Deep Potentials for Materials Science
TL;DR: Deep Potential (DP) as discussed by the authors is a recently developed type of machine learning potentials (MLP) method, which has been widely applied in computational materials science and has been shown to be useful in a wide range of materials systems.
Posted ContentDOI
Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution.
TL;DR: In this paper, a new deep potential-range correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical simulations of chemical reactions in the condensed phase was developed.
Journal ArticleDOI
New Insights and Predictions into Complex Homogeneous Reactions Enabled by Computational Chemistry in Synergy with Experiments: Isotopes and Mechanisms.
TL;DR: In this paper , the authors summarized new or deeper mechanistic insights (including isotope, dispersion, and dynamical effects) into several complex homogeneous reactions from their systematic computational studies along with subsequent experimental studies by different groups.
Journal ArticleDOI
Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology.
Claudia L Gómez-Flores,Denis Maag,Mayukh Kansari,Van Quan Vuong,Stephan Irle,Frauke Gräter,Tomáš Kubař,Marcus Elstner +7 more
TL;DR: An SRP for thiol-disulfide exchange based on an artificial neural network implementation in the DFTB+ software is developed and tested, demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of D FTB with an ANN only adds a small computational overhead.
Journal ArticleDOI
Combined QM/MM, Machine Learning Path Integral Approach to Compute Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage Reactions.
TL;DR: In this paper , a deep potential range correction (DPRc) model was proposed for combined quantum mechanical/molecular mechanical (QM/MM) simulations of reactions in the condensed phase.
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