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Molecular Structure

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Application of molecular docking and PSO-SVR intelligent approaches in antimalarial activity prediction of enantiomeric cycloguanil analogues.

SAR and QSAR in environmental research Nov 1, 2018
A series of antifolate compounds, i.e. 1-(4-chlorophenyl)-6,6-dimethyl-1,3,5-triazine-2,4-diamine, or cycloguanil analogues, have shown effective inhibiting properties against Plasmodium falciparum dihydrofolate reductase (PfDHFR). In this work, the ...
Molecular Structure Antimalarials Enzyme Inhibitors Molecular Docking Simulation Protein Binding Plasmodium falciparum Substrate Specificity Triazines Proguanil Tetrahydrofolate Dehydrogenase Quantitative Structure-Activity Relationship Stereoisomerism Folic Acid Antagonists Machine Learning Algorithms Reproducibility of Results
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Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates.

Journal of computer-aided molecular design Oct 26, 2018
Feature selection is commonly used as a preprocessing step to machine learning for improving learning performance, lowering computational complexity and facilitating model interpretation. This paper proposes the application of boosting feature select...
Machine Learning Algorithms Molecular Structure Ligands Quantitative Structure-Activity Relationship ATP Binding Cassette Transporter, Subfamily B Protein Binding Support Vector Machine Decision Trees
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Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics.

Analytical chemistry Oct 24, 2018
Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assi...
Models, Chemical Spectrometry, Mass, Electrospray Ionization Databases, Chemical Neural Networks, Computer Metabolomics Algorithms Chromatography, Liquid Computer Simulation Molecular Structure
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Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Machine Learning for Molecular Recognition of Myocardial Infarction.

Analytical chemistry Sep 25, 2018
Lipid profile changes in heart muscle have been previously linked to cardiac ischemia and myocardial infarction, but the spatial distribution of lipids and metabolites in ischemic heart remains to be fully investigated. We performed desorption electr...
Fatty Acids, Unsaturated Myocardial Infarction Spectrometry, Mass, Electrospray Ionization Molecular Imaging Machine Learning Female Animals Mice Molecular Structure
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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 ROC Curve Quantitative Structure-Activity Relationship Molecular Structure Models, Chemical Molecular Conformation
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Algorithmic Analysis of Cahn-Ingold-Prelog Rules of Stereochemistry: Proposals for Revised Rules and a Guide for Machine Implementation.

Journal of chemical information and modeling Aug 17, 2018
The most recent version of the Cahn-Ingold-Prelog rules for the determination of stereodescriptors as described in Nomenclature of Organic Chemistry: IUPAC Recommendations and Preferred Names 2013 (the "Blue Book"; Favre and Powell. Royal Society of ...
Stereoisomerism Structure-Activity Relationship Models, Molecular Molecular Structure Algorithms Computer Simulation Organic Chemicals Machine Learning Software
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Machine Learning in Drug Discovery.

Journal of chemical information and modeling Aug 15, 2018
Machine Learning Computer Simulation Drug Discovery Structure-Activity Relationship Molecular Structure Humans Neural Networks, Computer
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Recognition Tunneling of Canonical and Modified RNA Nucleotides for Their Identification with the Aid of Machine Learning.

ACS nano Jun 28, 2018
In the present study, we demonstrate a tunneling nanogap technique to identify individual RNA nucleotides, which can be used as a mechanism to read the nucleobases for direct sequencing of RNA in a solid-state nanopore. The tunneling nanogap is compo...
Nucleotides RNA Surface Properties Particle Size Molecular Structure Nanopores Machine Learning Nanotechnology Microscopy, Scanning Tunneling
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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...
Databases, Chemical Organometallic Compounds Thermodynamics Machine Learning Cyclization Algorithms Lithium Isoquinolines High-Throughput Screening Assays Neural Networks, Computer Linear Models Structure-Activity Relationship Molecular Structure Models, Chemical
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Predicting lysine-malonylation sites of proteins using sequence and predicted structural features.

Journal of computational chemistry May 14, 2018
Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylat...
Bacterial Proteins Lysine Malonates Hominidae Mice Saccharopolyspora Humans Protein Processing, Post-Translational Molecular Structure Machine Learning Animals
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