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SAR and QSAR in Environmental Research Volume 26, 2015 - Issue 12

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In silico exploratory study using structure–activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay

P. KamathDepartament d’Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, SpainCorrespondence[email protected]

G. RaitanoLaboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy

A. FernándezDepartament d’Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain

R. RalloDepartament d’Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Tarragona, Spain

E. BenfenatiLaboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy

Pages 1017-1031 | Received 28 Jul 2015, Accepted 13 Oct 2015, Published online: 13 Nov 2015

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https://doi.org/10.1080/1062936X.2015.1108932
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Charles Oluwaseun Adetunji, Daji Morumda, Wadzani Palnam Dauda, Peter Abraham, Elkanah Glen, Maryam Peter Abraham, Chidi Nnamdi Ifechukwude, Juliana Bunmi Adetunji, Kehinde Kazeem Kanmodi & Lawrence Achilles Nnyanzi. 2023. In silico Approaches to Macromolecular Chemistry. In silico Approaches to Macromolecular Chemistry 479 501 .

Priyanka Ramesh & Shanthi Veerappapillai. (2021) Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features. Applied Biochemistry and Biotechnology 193:12, pages 4018-4034.
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Anil Kumar Singh, Muhammad Bilal, Hafiz M.N. Iqbal & Abhay Raj. (2021) Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook. Science of The Total Environment 770, pages 144561.
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Diego Baderna, Domenico Gadaleta, Eleonora Lostaglio, Gianluca Selvestrel, Giuseppa Raitano, Azadi Golbamaki, Anna Lombardo & Emilio Benfenati. (2020) New in silico models to predict in vitro micronucleus induction as marker of genotoxicity. Journal of Hazardous Materials 385, pages 121638.
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Matteo Vian, Giuseppa Raitano, Alessandra Roncaglioni & Emilio Benfenati. (2019) In silico model for mutagenicity (Ames test), taking into account metabolism . Mutagenesis 34:1, pages 41-48.
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Romualdo Benigni, Chiara Laura Battistelli, Cecilia Bossa, Alessandro Giuliani, Elena Fioravanzo, Arianna Bassan, Mojca Fuart Gatnik, James Rathman, Chihae Yang & Olga Tcheremenskaia. (2019) Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across. EFSA Supporting Publications 16:3.
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Defang Fan, Hongbin Yang, Fuxing Li, Lixia Sun, Peiwen Di, Weihua Li, Yun Tang & Guixia Liu. (2018) In silico prediction of chemical genotoxicity using machine learning methods and structural alerts . Toxicology Research 7:2, pages 211-220.
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Melissa Van Bossuyt, Els Van Hoeck, Giuseppa Raitano, Serena Manganelli, Els Braeken, Gamze Ates, Tamara Vanhaecke, Sabine Van Miert, Emilio Benfenati, Birgit Mertens & Vera Rogiers. (2017) (Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances. Food and Chemical Toxicology 102, pages 109-119.
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In silico exploratory study using structure–activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay

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In silico exploratory study using structure–activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay

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