Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices.
about
New public QSAR model for carcinogenicity.Toxicological and clinical computational analysis and the US FDA/CDER.Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposureGenetic toxicology in the 21st century: reflections and future directions.Adaptation of high-throughput screening in drug discovery-toxicological screening tests.In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects.Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives.Comparison and possible use of in silico tools for carcinogenicity within REACH legislation.(Q)SAR modeling and safety assessment in regulatory review.Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities.In silico ADME/T modelling for rational drug design.Prediction of chemical carcinogenicity by machine learning approaches.Assessment of in silico models for acute aquatic toxicity towards fish under REACH regulation.Comparison of in silico tools for evaluating rat oral acute toxicity.Development and evaluation of a genomic signature for the prediction and mechanistic assessment of nongenotoxic hepatocarcinogens in the rat.In silico toxicological screening of natural products.Performance of (Q)SAR models for predicting Ames mutagenicity of aryl azo and benzidine based compounds.Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products.Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments.In Silico Screening of Chemicals for Genetic Toxicity Using MDL-QSAR, Nonparametric Discriminant Analysis, E-State, Connectivity, and Molecular Property Descriptors.Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree.Comparison of global and mode of action-based models for aquatic toxicity.Some findings relevant to the mechanistic interpretation in the case of predictive models for carcinogenicity based on the counter propagation artificial neural network.Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals.Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action-Based Predictions of Chemical Carcinogenesis in Rodents.QSAR models for anti-androgenic effect--a preliminary study.A hierarchical clustering methodology for the estimation of toxicity.Biomarkers of carcinogenicity and their roles in drug discovery and development.
P2860
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P2860
Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices.
description
2003 nî lūn-bûn
@nan
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
2003年论文
@zh
2003年论文
@zh-cn
name
Predicting the carcinogenic po ...... imilarity and E-state indices.
@en
type
label
Predicting the carcinogenic po ...... imilarity and E-state indices.
@en
prefLabel
Predicting the carcinogenic po ...... imilarity and E-state indices.
@en
P2093
P1476
Predicting the carcinogenic po ...... imilarity and E-state indices.
@en
P2093
Edwin J Matthews
Joseph F Contrera
R Daniel Benz
P304
P356
10.1016/S0273-2300(03)00071-0
P407
P577
2003-12-01T00:00:00Z