Investigation of the complex reaction coordinate of acid catalyzed amide hydrolysis from molecular dynamics simulations

2004 ◽  
Vol 300 (1-3) ◽  
pp. 79-83 ◽  
Author(s):  
Dirk Zahn
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Reaction Coordinate ◽  
Complex Reaction ◽  
Amide Hydrolysis ◽  
Acid Catalyzed ◽  
Dynamics Simulations

Fully Flexible Docking via Reaction-Coordinate-Independent Molecular Dynamics Simulations

2018 ◽  
Vol 58 (2) ◽  
pp. 490-500 ◽  
Author(s):  
Martina Bertazzo ◽  
Mattia Bernetti ◽  
Maurizio Recanatini ◽  
Matteo Masetti ◽  
Andrea Cavalli
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Reaction Coordinate ◽  
Flexible Docking ◽  
Dynamics Simulations

scholarly journals Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

2020 ◽  
Vol 11 (46) ◽  
pp. 12464-12476 ◽  
Author(s):  
Alex K. Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor M. Zavala ◽  
Reid C. Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Acid Catalyzed ◽  
Dynamics Simulations

Solvent-mediated, acid-catalyzed reaction rates relevant to the upgrading of biomass into high-value chemicals are accurately predicted using a combination of molecular dynamics simulations and 3D convolutional neural networks.

scholarly journals Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

2020 ◽  
Author(s):  
Alex Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor Zavala ◽  
Reid Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Solvent Mixtures ◽  
Acid Catalyzed ◽  
Solvent Systems ◽  
Dynamics Simulations

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable fast predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a computational implementation, which we call SolventNet, and train it using experimental reaction data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid screening of solvent systems and identification of improved biomass conversion conditions.

scholarly journals Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

2019 ◽  
Author(s):  
Alex Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor Zavala ◽  
Reid Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Solvent Mixtures ◽  
Acid Catalyzed ◽  
Solvent Systems ◽  
Dynamics Simulations

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable fast predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a computational implementation, which we call SolventNet, and train it using experimental reaction data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid screening of solvent systems and identification of improved biomass conversion conditions.

scholarly journals Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

2020 ◽  
Author(s):  
Alex Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor Zavala ◽  
Reid Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Dynamics Simulation ◽  
Acid Catalyzed ◽  
Solvent Systems ◽  
Dynamics Simulations

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable fast predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid. high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

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