AI Medical Compendium Journal:
Journal of chemical theory and computation

Showing 71 to 80 of 105 articles

Three-Dimensional Convolutional Neural Networks Utilizing Molecular Topological Features for Accurate Atomization Energy Predictions.

Journal of chemical theory and computation Feb 28, 2022
Deep learning methods provide a novel way to establish a correlation between two quantities. In this context, computer vision techniques such as three-dimensional (3D)-convolutional neural networks become a natural choice to associate a molecular pro...
Neural Networks, Computer Machine Learning
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PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations.

Journal of chemical theory and computation Feb 25, 2022
Understanding the process of ligand-protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting ...
Cluster Analysis Molecular Dynamics Simulation Algorithms Ligands Neural Networks, Computer
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GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling.

Journal of chemical theory and computation Feb 24, 2022
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations...
Deep Learning Workflow Thermodynamics Entropy Molecular Dynamics Simulation
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Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.

Journal of chemical theory and computation Feb 23, 2022
The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensiv...
Machine Learning Ligands Kinetics Protein Binding Molecular Dynamics Simulation
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Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides.

Journal of chemical theory and computation Feb 18, 2022
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has b...
Neural Networks, Computer Oligopeptides Molecular Conformation Molecular Dynamics Simulation Machine Learning
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Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO.

Journal of chemical theory and computation Nov 18, 2021
An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we appl...
Protein Binding Molecular Dynamics Simulation Peptides Algorithms Machine Learning
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TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks.

Journal of chemical theory and computation Oct 18, 2021
Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Pred...
Neural Networks, Computer Protein Structure, Secondary Databases, Protein Computational Biology Membrane Proteins Algorithms
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FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection.

Journal of chemical theory and computation Sep 23, 2021
Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. 2019, 59 (7), 3305-3315) proved that com...
Ligands Proteins Machine Learning Molecular Docking Simulation Algorithms Protein Binding
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Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.

Journal of chemical theory and computation Sep 1, 2021
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 mach...
Quantum Theory Thermodynamics Machine Learning
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Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy.

Journal of chemical theory and computation Jul 14, 2021
Computational capabilities are rapidly increasing, primarily because of the availability of GPU-based architectures. This creates unprecedented simulative possibilities for the systematic and robust computation of thermodynamic observables, including...
Molecular Dynamics Simulation Bridged-Ring Compounds Glycogen Synthase Kinase 3 beta Thermodynamics Ligands Machine Learning Protein Binding Imidazoles
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