AIMC Topic: 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 ...
Models, Molecular Molecular Conformation Ice Deep Learning
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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...
Hydrophobic and Hydrophilic Interactions Molecular Conformation Quantitative Structure-Activity Relationship Molecular Structure Pharmaceutical Preparations Drug Design Hydrogen Bonding Models, Molecular Machine Learning
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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...
Molecular Conformation beta-Lactams Models, Molecular Cyclodextrins Machine Learning Spectrum Analysis Structure-Activity Relationship
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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...
Aflatoxin B1 Arachis Photochemical Processes Molecular Conformation Electrochemistry Immunoassay Cobalt Food Analysis Sulfides Cadmium Compounds Models, Molecular Nanoparticles Feasibility Studies Oxides Food Contamination
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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...
Neural Networks, Computer Molecular Conformation Chromatin
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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 Deep Learning Molecular Conformation 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 Neural Networks, Computer Linear Models Molecular Conformation Models, Molecular
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Visualizing convolutional neural network protein-ligand scoring.

Journal of molecular graphics & modelling Jun 18, 2018
Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques c...
Molecular Conformation Proteins Models, Molecular Ligands ROC Curve Protein Binding Algorithms Deep Learning Neural Networks, Computer
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Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water.

Journal of chemical information and modeling May 16, 2018
Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent ...
Machine Learning Water Models, Molecular Drug Discovery Informatics Molecular Conformation Quantitative Structure-Activity Relationship Thermodynamics Pharmaceutical Preparations Gases Solvents
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Knowledge-Based Conformer Generation Using the Cambridge Structural Database.

Journal of chemical information and modeling Feb 22, 2018
Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geomet...
Hydrogen Bonding Databases, Chemical Knowledge Bases Molecular Conformation Software Proteins Databases, Protein Macrocyclic Compounds Algorithms Models, Molecular Ligands
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