AIMC Topic: Thermodynamics

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Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction.

Journal of chemical information and modeling Oct 25, 2016
Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach identifies electron sources and sinks among reactants and then ranks all source-sink pairs. T...
Quantum Theory Molecular Conformation Thermodynamics Machine Learning Models, Molecular
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A starfish robot based on soft and smart modular structure (SMS) actuated by SMA wires.

Bioinspiration & biomimetics Sep 9, 2016
This paper describes the design, fabrication and locomotion of a starfish robot whose locomotion principle is derived from a starfish. The starfish robot has a number of tentacles or arms extending from its central body in the form of a disk, like th...
Locomotion Alloys Thermodynamics Regeneration Biomimetic Materials Animals Equipment Design Models, Theoretical Gait Starfish Robotics
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Adaptive local learning in sampling based motion planning for protein folding.

BMC systems biology Aug 1, 2016
BACKGROUND: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods ...
Machine Learning Computational Biology Proteins Thermodynamics Models, Molecular Movement Protein Conformation Protein Folding
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Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.

Journal of chemical theory and computation Jun 3, 2016
A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry on...
Models, Molecular Amino Acids Quantum Theory Machine Learning Static Electricity Thermodynamics
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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...
Thermodynamics Molecular Dynamics Simulation Safety Protein Conformation Static Electricity Ligands Protein Binding Computational Biology Kinetics Machine Learning Drug Discovery
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SPECTRUS: A Dimensionality Reduction Approach for Identifying Dynamical Domains in Protein Complexes from Limited Structural Datasets.

Structure (London, England : 1993) Jul 9, 2015
Identifying dynamical, quasi-rigid domains in proteins provides a powerful means for characterizing functionally oriented structural changes via a parsimonious set of degrees of freedom. In fact, the relative displacements of few dynamical domains us...
Capsid Models, Molecular Software Internet Multifactor Dimensionality Reduction Adenylate Kinase Protein Structure, Tertiary Escherichia coli Thermodynamics Ion Channels Tobacco mosaic satellite virus
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High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm.

Journal of chemical theory and computation Apr 28, 2015
Artificial neural networks (NNs) represent a relatively recent approach for the prediction of molecular potential energies, suitable for simulations of large molecules and long time scales. By using NNs to fit electronic structure data, it is possibl...
Models, Theoretical Thermodynamics Neural Networks, Computer Algorithms
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Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

Journal of chemical theory and computation Apr 23, 2015
Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationa...
Isomerism Thermodynamics Ketones Quantum Theory Electrons Machine Learning
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Dicke simulators with emergent collective quantum computational abilities.

Physical review letters Apr 6, 2015
Using an approach inspired from spin glasses, we show that the multimode disordered Dicke model is equivalent to a quantum Hopfield network. We propose variational ground states for the system at zero temperature, which we conjecture to be exact in t...
Neural Networks, Computer Models, Theoretical Thermodynamics Glass Quantum Theory
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PENG: a neural gas-based approach for pharmacophore elucidation. method design, validation, and virtual screening for novel ligands of LTA4H.

Journal of chemical information and modeling Feb 9, 2015
The pharmacophore concept is commonly employed in virtual screening for hit identification. A pharmacophore is generally defined as the three-dimensional arrangement of the structural and physicochemical features of a compound responsible for its aff...
Crystallography, X-Ray Thermodynamics Protein Binding Algorithms Epoxide Hydrolases Benchmarking Aminopeptidases High-Throughput Screening Assays Humans Prospective Studies Enzyme Inhibitors Neural Networks, Computer Models, Molecular Reproducibility of Results Ligands
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