AI Medical Compendium Journal:
Journal of chemical theory and computation

Showing 81 to 90 of 105 articles

TopDomain: Exhaustive Protein Domain Boundary Metaprediction Combining Multisource Information and Deep Learning.

Journal of chemical theory and computation Jun 23, 2021
Protein domains are independent, functional, and stable structural units of proteins. Accurate protein domain boundary prediction plays an important role in understanding protein structure and evolution, as well as for protein structure prediction. C...
Algorithms Deep Learning Protein Domains Protein Conformation Proteins Computational Biology
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Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.

Journal of chemical theory and computation Apr 27, 2021
Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates...
Algorithms Programming Languages Datasets as Topic Nanostructures Supervised Machine Learning Protein Conformation Computational Chemistry Oligopeptides
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Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems.

Journal of chemical theory and computation Apr 5, 2021
Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are ...
Machine Learning Static Electricity Quantum Theory Molecular Dynamics Simulation
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Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.

Journal of chemical theory and computation Mar 30, 2021
Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly u...
Machine Learning HIV Protease Inhibitors HIV Protease Molecular Dynamics Simulation Mutation Drug Resistance, Viral
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Reconstruction of ARNT PAS-B Unfolding Pathways by Steered Molecular Dynamics and Artificial Neural Networks.

Journal of chemical theory and computation Mar 29, 2021
Several experimental studies indicated that large conformational changes, including partial domain unfolding, have a role in the functional mechanisms of the basic helix loop helix Per/ARNT/SIM (bHLH-PAS) transcription factors. Recently, single-molec...
Protein Unfolding Microscopy, Atomic Force Protein Conformation Molecular Dynamics Simulation Basic Helix-Loop-Helix Transcription Factors Neural Networks, Computer
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Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.

Journal of chemical theory and computation Mar 4, 2021
With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molecular processes. To handle the large simulation data sets and to accelerate slow, activated transitio...
Alanine Dipeptides Algorithms Neural Networks, Computer
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Insights into the Kinetic Partitioning Folding Dynamics of the Human Telomeric G-Quadruplex from Molecular Simulations and Machine Learning.

Journal of chemical theory and computation Aug 25, 2020
The human telomeric DNA G-quadruplex follows a kinetic partitioning folding mechanism. The underlying folding landscape potentially has many minima separated by high free-energy barriers. However, using current theoretical models to characterize this...
Humans G-Quadruplexes Machine Learning Telomere Molecular Dynamics Simulation Kinetics Nucleic Acid Conformation
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Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.

Journal of chemical theory and computation Jul 16, 2020
Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information ...
Machine Learning Molecular Dynamics Simulation Proteins
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Pair Potentials as Machine Learning Features.

Journal of chemical theory and computation Jul 6, 2020
Atom pairwise potential functions make up an essential part of many scoring functions for protein decoy detection. With the development of machine learning (ML) tools, there are multiple ways to combine potential functions to create novel ML models a...
Proteins Machine Learning
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Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.

Journal of chemical theory and computation Jun 29, 2020
Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of ...
Sulfur Deep Learning Density Functional Theory Halogens Thermodynamics Molecular Dynamics Simulation
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