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Public Databases Supporting Computational Toxicology
Journal of Toxicology and Environmental Health, Part B, 2010A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, researchers need quantitative in vitro and ideally in vivo data for large ...
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Applications of computers to toxicological research
Chemical Research in Toxicology, 1993Computers are used in toxicology in two ways. They are able to manage and manipulate large amounts of data, and it is because of this that they are used quite commonly to search toxicity databases. The mechanical ability of computers has led a number of organizations to pursue their use in regulatory compliance.
S, Wang, G W, Milne
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Testing computational toxicology models with phytochemicals
Molecular Nutrition & Food Research, 2010AbstractComputational toxicology employing quantitative structure–activity relationship (QSAR) modeling is an evidence‐based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its
Luis G, Valerio +5 more
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Computational Toxicology in EPA
2017Presentation in Iowa City at ...
0000-0003-1749-9971, Crofton, Kevin
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Computational Toxicology: Application in Environmental Chemicals
2012This chapter provides an overview of computational models that describe various aspects of the source-to-health effect continuum. Fate and transport models describe the release, transportation, and transformation of chemicals from sources of emission throughout the general environment.
Yu-Mei, Tan +6 more
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The evolving role of investigative toxicology in the pharmaceutical industry
Nature Reviews Drug Discovery, 2023Francois Pognan +2 more
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Machine Learning Methods in Computational Toxicology
2018Various 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 their use in computational toxicology.
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