Literature DB >> 30500930

Oy Vey! A Comment on "Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships Outperforming Animal Test Reproducibility".

Vinicius M Alves1, Joyce Borba1,2, Stephen J Capuzzi1, Eugene Muratov1,3, Carolina H Andrade2, Ivan Rusyn4, Alexander Tropsha1.   

Abstract

Mesh：

Year:  2019        PMID： 30500930      PMCID： PMC6317419          DOI： 10.1093/toxsci/kfy286

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN： 1096-0929            Impact factor:   4.849


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  4 in total

Review 1.  In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways.

Authors:  Jennifer Hemmerich; Gerhard F Ecker
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2020-03-31

2.  Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability.

Authors:  Anke Wilm; Conrad Stork; Christoph Bauer; Andreas Schepky; Jochen Kühnl; Johannes Kirchmair
Journal:  Int J Mol Sci       Date:  2019-09-28       Impact factor: 5.923

3.  STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity.

Authors:  Joyce V B Borba; Vinicius M Alves; Rodolpho C Braga; Daniel R Korn; Kirsten Overdahl; Arthur C Silva; Steven U S Hall; Erik Overdahl; Nicole Kleinstreuer; Judy Strickland; David Allen; Carolina Horta Andrade; Eugene N Muratov; Alexander Tropsha
Journal:  Environ Health Perspect       Date:  2022-02-22       Impact factor: 11.035

4.  Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.

Authors:  Arthur C Silva; Joyce V V B Borba; Vinicius M Alves; Steven U S Hall; Nicholas Furnham; Nicole Kleinstreuer; Eugene Muratov; Alexander Tropsha; Carolina Horta Andrade
Journal:  Artif Intell Life Sci       Date:  2021-12-05
  4 in total

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