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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.New public QSAR model for carcinogenicity.Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure Advances in computational methods to predict the biological activity of compounds.Computational approaches for innovative antiepileptic drug discovery.Evaluation of the OECD (Q)SAR Application Toolbox and Toxtree for predicting and profiling the carcinogenic potential of chemicals.Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products.Grouping of CoMPARA data with respect to compounds from the carcinogenic potency database.Predictivity and reliability of QSAR models: the case of mutagens and carcinogens.Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals.The application of molecular topology for ulcerative colitis drug discovery.PASS: identification of probable targets and mechanisms of toxicity†

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

http://www.wikidata.org/entity/Q42678649

description

2002 nî lūn-bûn

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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SAR and QSAR in Environmental Research

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

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Benigni R

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Richardt AM

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The Hallmarks of Cancer

Data quality in predictive toxicology: identification of chemical structures and calculation of chemical properties

Knowledge discovery and data mining in toxicology.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming

The NIEHS Predictive-Toxicology Evaluation Project.

New structural concepts for predicting carcinogenicity in rodents: an artificial intelligence approach.

Development of structure-activity relationship rules for predicting carcinogenic potential of chemicals.

Stratification of rodent carcinogenicity bioassay results to reflect relative human hazard.

Commercial toxicology prediction systems: a regulatory perspective.

Quantitative structure--activity relationship (QSAR) studies of mutagens and carcinogens.

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10.1080/10629360290002055

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1

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13

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