Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds.
Integrating computational methods to predict mutagenicity of aromatic azo compounds / D. Gadaleta, N. Porta, E. Vrontaki, S. Manganelli, A. Manganaro, G. Sello, M. Honma, E. Benfenati. - In: JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS. - ISSN 1059-0501. - 35:4(2017 Oct), pp. 239-257.
Integrating computational methods to predict mutagenicity of aromatic azo compounds
G. Sello;
2017
Abstract
Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds.File | Dimensione | Formato | |
---|---|---|---|
Integrating computational methods to predict mutagenicity of aromatic azo compounds-ja.pdf
accesso aperto
Tipologia:
Altro
Dimensione
892.72 kB
Formato
Adobe PDF
|
892.72 kB | Adobe PDF | Visualizza/Apri |
Integrating computational methods to predict mutagenicity of aromatic azo compounds.pdf
accesso riservato
Tipologia:
Publisher's version/PDF
Dimensione
1.31 MB
Formato
Adobe PDF
|
1.31 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.