Quantitative Structure-Activity Relationship
Showing 531 to 540 of 551 articles
Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.
SAR and QSAR in environmental research
Sep 1, 2017
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to ...
[Computational neuroanatomy and microstructure imaging using magnetic resonance imaging].
Der Nervenarzt
Aug 1, 2017
BACKGROUND: Current computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also g...
Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.
Oncotarget
Jul 25, 2017
Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) st...
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Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization-Support Vector Machine QSTR models.
Environmental science. Processes & impacts
Mar 22, 2017
According to the European REACH Directive, the acute toxicity towards Daphnia magna should be assessed for any industrial chemical with a market volume of more than 1 t/a. Therefore, it is highly recommended to determine the toxicity at a certain con...
Development of a Web-Enabled SVR-Based Machine Learning Platform and its Application on Modeling Transgene Expression Activity of Aminoglycoside-Derived Polycations.
Combinatorial chemistry & high throughput screening
Jan 1, 2017
OBJECTIVE: Support Vector Regression (SVR) has become increasingly popular in cheminformatics modeling. As a result, SVR-based machine learning algorithms, including Fuzzy-SVR and Least Square-SVR (LS-SVR) have been developed and applied in various r...
Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.
Molecular bioSystems
Nov 15, 2016
N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protei...
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Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.
SAR and QSAR in environmental research
Sep 1, 2016
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of ...
Probing the Hypothesis of SAR Continuity Restoration by the Removal of Activity Cliffs Generators in QSAR.
Current pharmaceutical design
Jan 1, 2016
In this work we report the first attempt to study the effect of activity cliffs over the generalization ability of machine learning (ML) based QSAR classifiers, using as study case a previously reported diverse and noisy dataset focused on drug induc...
5-Year Trends in QSAR and its Machine Learning Methods.
Current computer-aided drug design
Jan 1, 2016
BACKGROUND: Quantitative Structure-Activity Relationships (QSAR) is a well-established branch of computational chemistry. The presence of QSAR papers is decreasing for the last few years.
Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.
The Journal of toxicological sciences
Apr 1, 2015
Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental da...
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