Models, Chemical

Predicting drug target interactions using meta-path-based semantic network analysis.

BMC bioinformatics Apr 12, 2016

BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and li...

Machine Learning Models, Chemical Pharmaceutical Preparations Data Mining Pharmacological Phenomena Databases, Factual Drug Design Proteins

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Developing a support vector machine based QSPR model for prediction of half-life of some herbicides.

Ecotoxicology and environmental safety Mar 9, 2016

The half-life (t1/2) of 58 herbicides were modeled by quantitative structure-property relationship (QSPR) based molecular structure descriptors. After calculation and the screening of a large number of molecular descriptors, the most relevant those o...

Linear Models Quantitative Structure-Activity Relationship Half-Life Nonlinear Dynamics Herbicides Molecular Structure Models, Chemical Support Vector Machine

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Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation.

IEEE/ACM transactions on computational biology and bioinformatics Feb 11, 2016

Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of structural biology, which call for accurate computational prediction approaches. H...

Machine Learning Pattern Recognition, Automated Protein Structure, Secondary Molecular Sequence Data Databases, Protein Models, Chemical Amino Acid Sequence Algorithms Sequence Analysis, Protein Computer Simulation Membrane Proteins

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Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches.

Journal of pharmaceutical and biomedical analysis Jan 27, 2016

Peptides' retention time prediction is gaining increasing popularity in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics. This is a promising approach for improving successful proteome mapping, useful both in identification ...

Proteome Bacillus subtilis Chromatography, Liquid Humans Artificial Intelligence Peptides HeLa Cells Quantitative Structure-Activity Relationship Protein Conformation Models, Chemical Bacterial Proteins Tandem Mass Spectrometry

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Application of an Artificial Neural Network to the Prediction of OH Radical Reaction Rate Constants for Evaluating Global Warming Potential.

The journal of physical chemistry. B Dec 18, 2015

Rate constants for reactions of chemical compounds with hydroxyl radical are a key quantity used in evaluating the global warming potential of a substance. Experimental determination of these rate constants is essential, but it can also be difficult ...

Neural Networks, Computer Computer Simulation Temperature Hydroxyl Radical Models, Chemical Global Warming Kinetics

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A Sequence-Based Dynamic Ensemble Learning System for Protein Ligand-Binding Site Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 3, 2015

BACKGROUND: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands...

Models, Chemical Ligands Pattern Recognition, Automated Binding Sites Protein Interaction Mapping Sequence Analysis, Protein Machine Learning Protein Binding Algorithms Proteins Molecular Docking Simulation Data Interpretation, Statistical

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Machine Learning Estimation of Atom Condensed Fukui Functions.

Molecular informatics Nov 24, 2015

To enable the fast estimation of atom condensed Fukui functions, machine learning algorithms were trained with databases of DFT pre-calculated values for ca. 23,000 atoms in organic molecules. The problem was approached as the ranking of atom types w...

Machine Learning Models, Chemical Quantitative Structure-Activity Relationship

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Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

Journal of molecular graphics & modelling Nov 17, 2015

Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis ...

Bacillus anthracis Antigens, Bacterial Small Molecule Libraries Antidotes Models, Statistical High-Throughput Screening Assays Predictive Value of Tests User-Computer Interface Databases, Chemical Structure-Activity Relationship Bacterial Toxins Support Vector Machine Models, Chemical

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Prediction the Substrate Specificities of Membrane Transport Proteins Based on Support Vector Machine and Hybrid Features.

IEEE/ACM transactions on computational biology and bioinformatics Nov 11, 2015

Membrane transport proteins and their substrate specificities play crucial roles in a variety of cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to the protein-target interaction predictio...

Algorithms Molecular Docking Simulation Protein Binding Support Vector Machine Models, Chemical Substrate Specificity Pattern Recognition, Automated Protein Interaction Mapping Membrane Transport Proteins Sequence Analysis, Protein Binding Sites

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Prediction Enhancement of Residue Real-Value Relative Accessible Surface Area in Transmembrane Helical Proteins by Solving the Output Preference Problem of Machine Learning-Based Predictors.

Journal of chemical information and modeling Oct 20, 2015

The α-helical transmembrane proteins constitute 25% of the entire human proteome space and are difficult targets in high-resolution wet-lab structural studies, calling for accurate computational predictors. We present a novel sequence-based method ca...

Sequence Alignment Protein Structure, Quaternary Succinate Dehydrogenase Solvents Protein Structure, Secondary Membrane Proteins Molecular Sequence Data Humans Bacterial Proteins Databases, Protein Machine Learning Amino Acid Sequence Wolinella Models, Chemical Animals Solubility

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Predicting drug target interactions using meta-path-based semantic network analysis.

Developing a support vector machine based QSPR model for prediction of half-life of some herbicides.

Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation.

Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches.

Application of an Artificial Neural Network to the Prediction of OH Radical Reaction Rate Constants for Evaluating Global Warming Potential.

A Sequence-Based Dynamic Ensemble Learning System for Protein Ligand-Binding Site Prediction.

Machine Learning Estimation of Atom Condensed Fukui Functions.

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

Prediction the Substrate Specificities of Membrane Transport Proteins Based on Support Vector Machine and Hybrid Features.

Prediction Enhancement of Residue Real-Value Relative Accessible Surface Area in Transmembrane Helical Proteins by Solving the Output Preference Problem of Machine Learning-Based Predictors.

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