Toxnet

Fowler, Susan A.; Schnall, Janet G.

doi:10.1097/01.naj.0000443783.75162.79

Cited by 21 publications

(5 citation statements)

References 2 publications

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“…TOXNET is a comprehensive source that integrates several toxicity databases such as ToxLine and ChemIDplus (Fowler and Schnall, 2014 ). ACToR is a large database that aggregates data from thousands of public sources (Judson et al, 2008 ).…”

Section: Model Building With Machine Learning Methodsmentioning

confidence: 99%

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

et al. 2018

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During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

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Section: Model Building With Machine Learning Methodsmentioning

confidence: 99%

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

et al. 2018

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“…A condensed review of acute and chronic health effects, carcinogenicity, and first aid is also included under each consumer product. 5 A query for vinyl chloride containing household products in the HPD locates vinyl chloride containing pipe sealant products (Figure 7). Since vinyl chloride is not the only component of these products, the HPD links to the additional component information for review.…”

Section: Household Products Database Http://hpdnlmnihgov/mentioning

confidence: 99%

What Online Toxicology Resources Are Available at No Cost From the (US) National Library of Medicine to Assist Practicing OEM Physicians?

Downs

Hakkinen

2015

Journal of Occupational &Amp; Environmental Medicine

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“…Lastly, the U.S. National Library of Medicine’s Toxicology Data Network (TOXNET) is a publicly available resource that allows users to screen specific compounds against a large variety of toxicology databases (HSDB, CCRIS, GENETOX, etc.) and literature references (TOXLINE, DART) [31,32]. More of a data mining than predictive tool, the TOXNET databases are useful for assessing hit compounds from virtual or experimental screening during the hit validation process to assess a compound for further advancement.…”

Section: Applicationsmentioning

confidence: 99%

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation

Hevener

2018

Methods in Molecular Biology

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The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process, is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This chapter focuses on the application of computational molecular filters, applied either prescreening or postscreening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.

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Toxnet

Abstract: A reliable source of information about potentially harmful substances.

Cited by 21 publications

References 2 publications

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

What Online Toxicology Resources Are Available at No Cost From the (US) National Library of Medicine to Assist Practicing OEM Physicians?

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation

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