AIMC Topic: Molecular Conformation

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Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods.

Chemical research in toxicology Apr 9, 2019
Saponins are a type of compounds bearing a hydrophobic steroid/triterpenoid moiety and hydrophilic carbohydrate branches. The majority of the saponins demonstrate a broad range of prominent pharmacological activities. Nevertheless, many saponins also...
Humans Machine Learning Hemolysis Erythrocytes Molecular Conformation Saponins Hydrophobic and Hydrophilic Interactions
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Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks.

Journal of chemical information and modeling Apr 8, 2019
Several recent reports have shown that long short-term memory generative neural networks (LSTM) of the type used for grammar learning efficiently learn to write Simplified Molecular Input Line Entry System (SMILES) of druglike compounds when trained ...
Cheminformatics Neural Networks, Computer Models, Molecular Pharmaceutical Preparations Molecular Conformation
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Machine Learning Prediction of H Adsorption Energies on Ag Alloys.

Journal of chemical information and modeling Apr 2, 2019
Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, ...
Adsorption Models, Molecular Machine Learning Hydrogen Alloys Thermodynamics Silver Molecular Conformation
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DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules.

Journal of chemical information and modeling Mar 27, 2019
Computer simulation studies of multiphase systems rely on the accurate identification of local molecular structures and arrangements in order to extract useful insights. Local order parameters, such as Steinhardt parameters, are widely used for this ...
Deep Learning Ice Models, Molecular Molecular Conformation
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Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling Feb 28, 2019
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...
Hydrogen Bonding Molecular Conformation Quantitative Structure-Activity Relationship Models, Molecular Molecular Structure Pharmaceutical Preparations Hydrophobic and Hydrophilic Interactions Machine Learning Drug Design
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Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

Molecules (Basel, Switzerland) Feb 19, 2019
The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, includ...
Machine Learning Molecular Conformation Structure-Activity Relationship beta-Lactams Cyclodextrins Spectrum Analysis Models, Molecular
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Etching reaction-based photoelectrochemical immunoassay of aflatoxin B in foodstuff using cobalt oxyhydroxide nanosheets-coating cadmium sulfide nanoparticles as the signal tags.

Analytica chimica acta Dec 7, 2018
A new split-type photoelectrochemical (PEC) immunosensing platform was designed for sensitive detection of aflatoxin B (AFB) in foodstuffs, coupling with enzymatic hydrolysate-triggered etching reaction of cobalt oxyhydroxide (CoOOH) on cadmium sulfi...
Molecular Conformation Oxides Electrochemistry Photochemical Processes Food Analysis Cadmium Compounds Immunoassay Cobalt Nanoparticles Models, Molecular Feasibility Studies Aflatoxin B1 Sulfides Food Contamination Arachis
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Dense neural networks for predicting chromatin conformation.

BMC bioinformatics Oct 11, 2018
BACKGROUND: DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distr...
Molecular Conformation Chromatin Neural Networks, Computer
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Quantitative structure-activity relationship analysis using deep learning based on a novel molecular image input technique.

Bioorganic & medicinal chemistry letters Aug 27, 2018
Quantitative structure-activity relationship (QSAR) analysis uses structural, quantum chemical, and physicochemical features calculated from molecular geometry as explanatory variables predicting physiological activity. Recently, deep learning based ...
ROC Curve Molecular Structure Models, Chemical Molecular Conformation Deep Learning Quantitative Structure-Activity Relationship
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3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction.

Journal of physics. Condensed matter : an Institute of Physics journal Jul 2, 2018
In this work, we present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These s...
Thermodynamics Linear Models Molecular Conformation Neural Networks, Computer Models, Molecular
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