Literature DB >> 18469376

A novel approach: chemical relational databases, and the role of the ISSCAN database on assessing chemical carcinogenicity.

Romualdo Benigni1, Cecilia Bossa, Ann M Richard, Chihae Yang.   

Abstract

Mutagenicity and carcinogenicity databases are crucial resources for toxicologists and regulators involved in chemicals risk assessment. Until recently, existing public toxicity databases have been constructed primarily as "look-up-tables" of existing data, and most often did not contain chemical structures. Concepts and technologies originated from the structure-activity relationships science have provided powerful tools to create new types of databases, where the effective linkage of chemical toxicity with chemical structure can facilitate and greatly enhance data gathering and hypothesis generation, by permitting: a) exploration across both chemical and biological domains; and b) structure-searchability through the data. This paper reviews the main public databases, together with the progress in the field of chemical relational databases, and presents the ISSCAN database on experimental chemical carcinogens.

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Year:  2008        PMID: 18469376

Source DB:  PubMed          Journal:  Ann Ist Super Sanita        ISSN: 0021-2571            Impact factor:   1.663


  7 in total

1.  Genetic toxicology in silico protocol.

Authors:  Catrin Hasselgren; Ernst Ahlberg; Yumi Akahori; Alexander Amberg; Lennart T Anger; Franck Atienzar; Scott Auerbach; Lisa Beilke; Phillip Bellion; Romualdo Benigni; Joel Bercu; Ewan D Booth; Dave Bower; Alessandro Brigo; Zoryana Cammerer; Mark T D Cronin; Ian Crooks; Kevin P Cross; Laura Custer; Krista Dobo; Tatyana Doktorova; David Faulkner; Kevin A Ford; Marie C Fortin; Markus Frericks; Samantha E Gad-McDonald; Nichola Gellatly; Helga Gerets; Véronique Gervais; Susanne Glowienke; Jacky Van Gompel; James S Harvey; Jedd Hillegass; Masamitsu Honma; Jui-Hua Hsieh; Chia-Wen Hsu; Tara S Barton-Maclaren; Candice Johnson; Robert Jolly; David Jones; Ray Kemper; Michelle O Kenyon; Naomi L Kruhlak; Sunil A Kulkarni; Klaus Kümmerer; Penny Leavitt; Scott Masten; Scott Miller; Chandrika Moudgal; Wolfgang Muster; Alexandre Paulino; Elena Lo Piparo; Mark Powley; Donald P Quigley; M Vijayaray Reddy; Andrea-Nicole Richarz; Benoit Schilter; Ronald D Snyder; Lidiya Stavitskaya; Reinhard Stidl; David T Szabo; Andrew Teasdale; Raymond R Tice; Alejandra Trejo-Martin; Anna Vuorinen; Brian A Wall; Pete Watts; Angela T White; Joerg Wichard; Kristine L Witt; Adam Woolley; David Woolley; Craig Zwickl; Glenn J Myatt
Journal:  Regul Toxicol Pharmacol       Date:  2019-06-11       Impact factor: 3.271

2.  In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions.

Authors:  Enrico Mombelli; Giuseppa Raitano; Emilio Benfenati
Journal:  Methods Mol Biol       Date:  2022

3.  Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset.

Authors:  Prachi Pradeep; Richard Judson; David M DeMarini; Nagalakshmi Keshava; Todd M Martin; Jeffry Dean; Catherine F Gibbons; Anita Simha; Sarah H Warren; Maureen R Gwinn; Grace Patlewicz
Journal:  Comput Toxicol       Date:  2021-05-01

4.  A graph neural network approach for molecule carcinogenicity prediction.

Authors:  Philip Fradkin; Adamo Young; Lazar Atanackovic; Brendan Frey; Leo J Lee; Bo Wang
Journal:  Bioinformatics       Date:  2022-06-24       Impact factor: 6.931

5.  CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

Authors:  Li Zhang; Haixin Ai; Wen Chen; Zimo Yin; Huan Hu; Junfeng Zhu; Jian Zhao; Qi Zhao; Hongsheng Liu
Journal:  Sci Rep       Date:  2017-05-18       Impact factor: 4.379

6.  QSAR ligand dataset for modelling mutagenicity, genotoxicity, and rodent carcinogenicity.

Authors:  Davy Guan; Kevin Fan; Ian Spence; Slade Matthews
Journal:  Data Brief       Date:  2018-02-02

7.  Virtual Extensive Read-Across: A New Open-Access Software for Chemical Read-Across and Its Application to the Carcinogenicity Assessment of Botanicals.

Authors:  Edoardo Luca Viganò; Erika Colombo; Giuseppa Raitano; Alberto Manganaro; Alessio Sommovigo; Jean Lou Cm Dorne; Emilio Benfenati
Journal:  Molecules       Date:  2022-10-05       Impact factor: 4.927

  7 in total

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