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Journal of chemical theory and computation

Showing 91 to 100 of 105 articles

Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies.

Journal of chemical theory and computation Jun 4, 2020
Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on ...
Algorithms Drug Design Protein Binding Humans Machine Learning Thermodynamics Protein Structure, Tertiary Epoxide Hydrolases Molecular Dynamics Simulation Ligands
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Machine Learning Driven Analysis of Large Scale Simulations Reveals Conformational Characteristics of Ubiquitin Chains.

Journal of chemical theory and computation Apr 7, 2020
Understanding the conformational characteristics of protein complexes in solution is crucial for a deeper insight in their biological function. Molecular dynamics simulations performed on high performance computing plants and with modern simulation t...
Machine Learning Protein Conformation Ubiquitin Molecular Dynamics Simulation
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Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation Jan 16, 2020
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...
Humans Machine Learning Protein Binding HIV-1 Ligands Drug Resistance, Viral Mutation Molecular Dynamics Simulation Monte Carlo Method Hydrogen Bonding Static Electricity HIV Protease HIV Protease Inhibitors
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Finding Reactive Configurations: A Machine Learning Approach for Estimating Energy Barriers Applied to Sirtuin 5.

Journal of chemical theory and computation Nov 25, 2019
Sirtuin 5 is a class III histone deacetylase that, unlike its classification, mainly catalyzes desuccinylation and demanoylation reactions. It is an interesting drug target that we use here to test new ideas for calculating reaction pathways of large...
Thermodynamics Sirtuins Humans Machine Learning Quantum Theory Protein Conformation Molecular Dynamics Simulation
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Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.

Journal of chemical theory and computation Oct 7, 2019
Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that ar...
1,2-Dipalmitoylphosphatidylcholine Lipid Bilayers Molecular Dynamics Simulation Phosphatidylcholines Unsupervised Machine Learning Cholesterol Lipids
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PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges.

Journal of chemical theory and computation May 14, 2019
In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio reference data, these methods allow for accurately predicting the properties of chemical systems,...
Neural Networks, Computer Static Electricity Thermodynamics Algorithms Quantum Theory Molecular Dynamics Simulation Proteins Protein Conformation
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Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Journal of chemical theory and computation Jan 7, 2019
Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quantum mechanical (QM) calculations is challenging due to the complexity of proteins and high computati...
Quantum Theory Dipeptides Thermodynamics Static Electricity Amino Acids Molecular Dynamics Simulation Proteins Models, Chemical Neural Networks, Computer Peptides
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Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE.

Journal of chemical theory and computation Dec 24, 2018
In this work, we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 2018, 149...
Deep Learning Thermodynamics Protein Binding Molecular Dynamics Simulation Proteins Ligands
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TopScore: Using Deep Neural Networks and Large Diverse Data Sets for Accurate Protein Model Quality Assessment.

Journal of chemical theory and computation Oct 9, 2018
The value of protein models obtained with automated protein structure prediction depends primarily on their accuracy. Protein model quality assessment is thus critical to select the model that can best answer biologically relevant questions from an e...
Models, Molecular Neural Networks, Computer Protein Conformation Proteins Databases, Protein
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Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.

Journal of chemical theory and computation Aug 17, 2018
The ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learn...
Molecular Dynamics Simulation Models, Chemical Neural Networks, Computer Spectrophotometry, Infrared Methanol Proteins
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