A structural feature-based computational approach for toxicology predictions. - Wikidata - awgldk - TriplyDB

A structural feature-based computational approach for toxicology predictions.

A structural feature-based computational approach for toxicology predictions.

http://www.wikidata.org/entity/Q37674610

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A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening Assays Prediction of off-target effects on angiotensin-converting enzyme 2.Cell-Based High-Throughput Screening for Aromatase Inhibitors in the Tox21 10K Library In silico toxicology models and databases as FDA Critical Path Initiative toolkits.The computational prediction of genotoxicity.Application of advanced in silico methods for predictive modeling and information integration.Structure-based design and biochemical evaluation of sulfanilamide derivatives as hepatitis B virus capsid assembly inhibitors.Development of cardiac safety translational tools for QT prolongation and torsade de pointes.Computational science in drug metabolism and toxicology.Qualitative consensus of QSAR ready biodegradability predictions

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A structural feature-based computational approach for toxicology predictions.

http://www.wikidata.org/entity/Q37674610

description

article científic

@ca

article scientifique

@fr

articolo scientifico

@it

artigo científico

@pt

bilimsel makale

@tr

scientific article published on April 2010

@en

vedecký článok

@sk

vetenskaplig artikel

@sv

videnskabelig artikel

@da

vědecký článek

@cs

name

A structural feature-based computational approach for toxicology predictions.

@en

A structural feature-based computational approach for toxicology predictions.

@nl

type

label

A structural feature-based computational approach for toxicology predictions.

@en

A structural feature-based computational approach for toxicology predictions.

@nl

prefLabel

A structural feature-based computational approach for toxicology predictions.

@en

A structural feature-based computational approach for toxicology predictions.

@nl

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Expert Opinion on Drug Metabolism & Toxicology

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A structural feature-based computational approach for toxicology predictions.

@en

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Chihae Yang

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Kirk B Arvidson

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Luis G Valerio

http://www.w3.org/2001/XMLSchema#string

Naomi L Kruhlak

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Structure-activity relationship approaches and applications.

The use of structure-activity relationship analysis in the food contact notification program.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling.

In silico prediction of ADMET properties: how far have we come?

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505-518

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10.1517/17425250903499286

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4

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2010-04-01T00:00:00Z

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20074001

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