AIMC Topic: Molecular Dynamics Simulation

Clear Filters Showing 591 to 600 of 710 articles

Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics.

Journal of chemical theory and computation May 4, 2017
The free energy calculations of complex chemical and biological systems with molecular dynamics (MD) are inefficient due to multiple local minima separated by high-energy barriers. The minima can be escaped using an enhanced sampling method such as m...
Neural Networks, Computer Molecular Dynamics Simulation Thermodynamics
View on PubMed DOI

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

Proteins Mar 21, 2017
Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during t...
Software Double-Blind Method Internet Computational Biology Benchmarking Support Vector Machine Models, Statistical Proteins Molecular Dynamics Simulation Protein Conformation Monte Carlo Method
View on PubMed DOI

Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

ACS combinatorial science Jan 9, 2017
Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting prop...
Polymers Neural Networks, Computer Humans Machine Learning Molecular Dynamics Simulation Algorithms
View on PubMed DOI

Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics.

Journal of chemical information and modeling May 20, 2016
One of the unaddressed challenges in drug discovery is that drug potency determined in vitro is not a reliable indicator of drug activity in vivo. Accumulated evidence suggests that in vivo activity is more strongly correlated with the binding/unbind...
Safety Ligands Protein Conformation Kinetics Molecular Dynamics Simulation Static Electricity Thermodynamics Machine Learning Drug Discovery Protein Binding Computational Biology
View on PubMed DOI

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles.

Journal of chemical information and modeling May 3, 2016
Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates ac...
User-Computer Interface Molecular Dynamics Simulation Drug Evaluation, Preclinical Machine Learning Protein Conformation
View on PubMed DOI

Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.

Journal of chemical information and modeling Apr 14, 2016
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires cl...
Protein Conformation Protein Binding Supervised Machine Learning Ligands Cell Membrane Receptors, G-Protein-Coupled Molecular Dynamics Simulation
View on PubMed DOI

Clustering molecular dynamics trajectories for optimizing docking experiments.

Computational intelligence and neuroscience Mar 22, 2015
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the f...
Molecular Dynamics Simulation Ligands Algorithms Cluster Analysis Proteins Artificial Intelligence Software
View on PubMed DOI

The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.

Nature communications Jan 27, 2015
The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to...
Cluster Analysis Kinetics Molecular Dynamics Simulation Time Factors Humans Protein Binding Pyrrolidines Machine Learning Protein Multimerization Ligands Algorithms Purine-Nucleoside Phosphorylase Pyrimidinones Thermodynamics
View on PubMed DOI

Types and effects of protein variations.

Human genetics Jan 24, 2015
Variations in proteins have very large number of diverse effects affecting sequence, structure, stability, interactions, activity, abundance and other properties. Although protein-coding exons cover just over 1 % of the human genome they harbor an di...
Structure-Activity Relationship Models, Molecular Molecular Dynamics Simulation Amino Acid Sequence Genetic Variation Protein Processing, Post-Translational Proteins Databases, Protein Humans Animals Protein Folding Protein Structure, Quaternary Gene Ontology Protein Structure, Secondary
View on PubMed DOI

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.

Physical chemistry chemical physics : PCCP Dec 1, 2014
Investigating the properties of protons in water is essential for understanding many chemical processes in aqueous solution. While important insights can in principle be gained by accurate and well-established methods like ab initio molecular dynamic...
Water Molecular Dynamics Simulation Hydrogen Bonding Thermodynamics Protons Neural Networks, Computer
View on PubMed DOI
Popular Topics
Recent Journals