AIMC Topic: Models, Chemical

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In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods.

ChemMedChem Sep 21, 2018
The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all ...
Algorithms Computer Simulation Models, Chemical Permeability Databases, Chemical Blood-Brain Barrier Organic Chemicals Support Vector Machine
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Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning.

Analytical chemistry Aug 29, 2018
The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this predict...
HeLa Cells Chromatography, Reverse-Phase Humans Peptides Deep Learning Proteomics Models, Chemical Amino Acid Sequence
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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 ...
Deep Learning Models, Chemical ROC Curve Molecular Conformation Molecular Structure Quantitative Structure-Activity Relationship
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Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.

Journal of chemical theory and computation Aug 17, 2018
The ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learn...
Neural Networks, Computer Proteins Molecular Dynamics Simulation Spectrophotometry, Infrared Models, Chemical Methanol
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Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

Journal of chemical information and modeling Aug 15, 2018
The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically ...
Databases, Pharmaceutical Algorithms Drug Discovery Drug-Related Side Effects and Adverse Reactions Deep Learning Small Molecule Libraries Models, Chemical Computer Graphics Humans Pharmaceutical Preparations Models, Biological
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Label-Free Biomolecule Detection in Physiological Solutions With Enhanced Sensitivity Using Graphene Nanogrids FET Biosensor.

IEEE transactions on nanobioscience Aug 6, 2018
Recently, graphene nanogrid sensor has been reported to be capable of sub-femtomolar sensing of Hepatitis B (Hep-B) surface antigen in buffer. However, for such low concentration of Hep-B in serum, it has been observed during real-time operation that...
Limit of Detection Models, Chemical Equipment Design Humans Signal Processing, Computer-Assisted Neural Networks, Computer Hepatitis B Surface Antigens Biosensing Techniques Graphite
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Modeling of an activated sludge process for effluent prediction-a comparative study using ANFIS and GLM regression.

Environmental monitoring and assessment Aug 1, 2018
In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive mod...
Organic Chemicals Environmental Monitoring Fuzzy Logic Neural Networks, Computer Wastewater Water Pollution, Chemical Models, Chemical Sewage Linear Models
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Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Journal of chemical information and modeling Jun 27, 2018
Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturba...
Linear Models Databases, Chemical Organometallic Compounds Thermodynamics Cyclization Models, Chemical Machine Learning Isoquinolines Algorithms High-Throughput Screening Assays Structure-Activity Relationship Lithium Molecular Structure Neural Networks, Computer
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Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations.

Journal of chemical information and modeling Mar 7, 2018
Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual paramet...
Quantum Theory Electrons Static Electricity Pharmaceutical Preparations Molecular Dynamics Simulation Machine Learning Models, Chemical Thermodynamics Volatilization
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A modified artificial neural network based prediction technique for tropospheric radio refractivity.

PloS one Mar 1, 2018
Radio refractivity plays a significant role in the development and design of radio systems for attaining the best level of performance. Refractivity in the troposphere is one of the features affecting electromagnetic waves, and hence the communicatio...
Humidity Atmosphere Radio Waves Temperature Radio Neural Networks, Computer Models, Chemical Algorithms Pressure
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