AIMC Topic: Molecular Dynamics Simulation

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Fast and accurate prediction of partial charges using Atom-Path-Descriptor-based machine learning.

Bioinformatics (Oxford, England) Sep 15, 2020
MOTIVATION: Partial atomic charges are usually used to calculate the electrostatic component of energy in many molecular modeling applications, such as molecular docking, molecular dynamics simulations, free energy calculations and so forth. High-lev...
Molecular Docking Simulation Molecular Dynamics Simulation Static Electricity Machine Learning Software
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Artificial neural networks for the inverse design of nanoparticles with preferential nano-bio behaviors.

The Journal of chemical physics Aug 7, 2020
Safe and efficient use of ultrasmall nanoparticles (NPs) in biomedicine requires numerous independent conditions to be met, including colloidal stability, selectivity for proteins and membranes, binding specificity, and low affinity for plasma protei...
Molecular Dynamics Simulation Lipid Bilayers Nanoparticles Proteins Neural Networks, Computer Protein Binding
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Artificial intelligence-based multi-objective optimization protocol for protein structure refinement.

Bioinformatics (Oxford, England) Jan 15, 2020
MOTIVATION: Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optim...
Molecular Dynamics Simulation Monte Carlo Method Artificial Intelligence Algorithms Proteins
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evERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

The Journal of chemical physics Dec 7, 2019
Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that s...
Protein Binding Proteins Molecular Dynamics Simulation Machine Learning Protein Conformation
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refineD: improved protein structure refinement using machine learning based restrained relaxation.

Bioinformatics (Oxford, England) Sep 15, 2019
MOTIVATION: Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraint...
Proteins Molecular Dynamics Simulation Machine Learning Software Protein Conformation
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Microsoft makes splash in AI-enabled lab solutions.

Nature biotechnology Aug 1, 2019
Cell Line Molecular Dynamics Simulation Mammals Synthetic Biology Animals Drug Design Algorithms Biomedical Research Machine Learning
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Exploring the Scoring Function Space.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the ch...
Proteins Web Browser Machine Learning Algorithms Molecular Dynamics Simulation Molecular Docking Simulation Software
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Machine Learning to Predict Binding Affinity.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic ...
Algorithms Molecular Docking Simulation Humans Machine Learning Databases, Protein Software Molecular Dynamics Simulation Models, Statistical Ligands Web Browser Proteins Supervised Machine Learning Cyclin-Dependent Kinase 2 HIV Protease
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Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions.

Protein and peptide letters Jan 1, 2019
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to det...
Machine Learning Algorithms Computational Biology Hydrophobic and Hydrophilic Interactions RNA-Binding Proteins Nucleic Acid Conformation Molecular Dynamics Simulation RNA
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[An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-layer Neural Networks].

Molekuliarnaia biologiia Jan 1, 2019
A good scoring function is necessary for ab inito prediction of RNA tertiary structures. In this study, we explored the power of a machine learning based approach as a scoring function. Compared with the traditional scoring functions, the present app...
Neural Networks, Computer Nucleic Acid Conformation RNA Molecular Dynamics Simulation
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