A structural feature-based computational approach for toxicology predictions.
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A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening AssaysPrediction of off-target effects on angiotensin-converting enzyme 2.Cell-Based High-Throughput Screening for Aromatase Inhibitors in the Tox21 10K LibraryIn 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
P2860
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P2860
A structural feature-based computational approach for toxicology predictions.
description
article científic
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article scientifique
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articolo scientifico
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artigo científico
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bilimsel makale
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scientific article published on April 2010
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
A structural feature-based computational approach for toxicology predictions.
@en
A structural feature-based computational approach for toxicology predictions.
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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
P2093
P2860
P1476
A structural feature-based computational approach for toxicology predictions.
@en
P2093
Chihae Yang
Kirk B Arvidson
Luis G Valerio
Naomi L Kruhlak
P2860
P304
P356
10.1517/17425250903499286
P407
P577
2010-04-01T00:00:00Z