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Deep learning for predicting toxicity of chemicals: a mini review.

Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews Mar 1, 2019
Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-through...
Environmental Pollutants Machine Learning Toxicity Tests Humans Deep Learning High-Throughput Screening Assays Neural Networks, Computer Models, Chemical Quantitative Structure-Activity Relationship
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PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

Journal of computational chemistry Feb 15, 2019
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets...
Protein Binding Databases, Protein Models, Chemical Amino Acid Sequence Computational Biology Support Vector Machine Proteins
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Quantitative design rules for protein-resistant surface coatings using machine learning.

Scientific reports Jan 22, 2019
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the ...
Equipment Design Polymers Fibrinogen Machine Learning Nanoparticles Models, Chemical Immobilized Proteins Surface Properties Muramidase Datasets as Topic Biofouling Linear Models
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Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Journal of chemical theory and computation Jan 7, 2019
Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quantum mechanical (QM) calculations is challenging due to the complexity of proteins and high computati...
Static Electricity Thermodynamics Molecular Dynamics Simulation Quantum Theory Neural Networks, Computer Dipeptides Proteins Peptides Models, Chemical Amino Acids
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Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.

BMC bioinformatics Dec 31, 2018
BACKGROUND: Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph ...
Humans Neural Networks, Computer Software Pharmaceutical Preparations Proteins Binding Sites DNA Models, Chemical Deep Learning Protein Binding
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Spatiotemporal continuous estimates of PM concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations.

Environment international Dec 18, 2018
Ambient exposure to fine particulate matter (PM) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM concentrat...
Particulate Matter Satellite Communications Aerosols Models, Statistical Air Pollutants Models, Chemical China Environmental Monitoring Humans Machine Learning Risk Assessment Public Health
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Machine-Learning-Based Cyclic Voltammetry Behavior Model for Supercapacitance of Co-Doped Ceria/rGO Nanocomposite.

Journal of chemical information and modeling Dec 5, 2018
This paper examines the cobalt-doped ceria/reduced graphene oxide (Co-CeO/rGO) nanocomposite as a supercapacitor and modeling of its cyclic voltammetry behavior using Artificial Neural Network (ANN) and Random Forest Algorithm (RFA). Good agreement w...
Cerium Electrochemistry Electric Capacitance Cobalt Graphite Machine Learning Models, Chemical Nanocomposites
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A Neural Network QSPR Model for Accurate Prediction of Flash Point of Pure Hydrocarbons.

Molecular informatics Nov 29, 2018
The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the mos...
Quantitative Structure-Activity Relationship Models, Chemical Hydrocarbons Neural Networks, Computer Drug Discovery
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Bimolecular Nucleophilic Substitution Reactions: Predictive Models for Rate Constants and Molecular Reaction Pairs Analysis.

Molecular informatics Nov 23, 2018
Here, we report the data visualization, analysis and modeling for a large set of 4830 S 2 reactions the rate constant of which (logk) was measured at different experimental conditions (solvent, temperature). The reactions were encoded by one single m...
Support Vector Machine Oxidation-Reduction Models, Chemical Kinetics Hydrocarbons, Cyclic
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Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.

Environmental science and pollution research international Nov 7, 2018
River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network model...
Models, Chemical Water Quality Rivers Environmental Monitoring Fuzzy Logic Cluster Analysis Temperature Water Neural Networks, Computer Machine Learning Algorithms
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