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Quantitative Structure-Activity Relationship

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Machine Learning Methods in Computational Toxicology.

Methods in molecular biology (Clifton, N.J.)
Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of ...

[Construction of a High-precision Chemical Prediction System Using Human ESCs].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
 Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells...

Machine Learning-Based Modeling of Drug Toxicity.

Methods in molecular biology (Clifton, N.J.)
Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accura...

Editor's Highlight: Identification of Any Structure-Specific Hepatotoxic Potential of Different Pyrrolizidine Alkaloids Using Random Forests and Artificial Neural Networks.

Toxicological sciences : an official journal of the Society of Toxicology
Pyrrolizidine alkaloids (PAs) are characteristic metabolites of some plant families and form a powerful defense mechanism against herbivores. More than 600 different PAs are known. PAs are ester alkaloids composed of a necine base and a necic acid, w...

In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method.

SAR and QSAR in environmental research
The cytochrome P450 (CYP) enzyme superfamily is involved in phase I metabolism which chemically modifies a variety of substrates via oxidative reactions to make them more water-soluble and easier to eliminate. Inhibition of these enzymes leads to und...

How good are publicly available web services that predict bioactivity profiles for drug repurposing?

SAR and QSAR in environmental research
Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely ava...

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

SAR and QSAR in environmental research
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
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
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...

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
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...