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SAR and QSAR in Environmental Research Volume 27, 2016 - Issue 5
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Articles

Results of a round-robin exercise on read-across

E. BenfenatiIRCCS – Istituto di Ricerche Farmacologiche Mario Negri, Milano, ItalyCorrespondence[email protected]
,
M. BelliIRCCS – Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
,
T. BorgesDirecção-Geral da Saúde, Lisboa, Portugal
,
E. CasimiroINFOTOX, Consultores de Riscos Ambientais e Tecnológicos, Lda, Lisboa, Portugal
,
J. CesterUniversitat Rovira i Virgili, Tarragona, Spain
,
A. FernandezUniversitat Rovira i Virgili, Tarragona, Spain
,
G. GiniPolitecnico di Milano, Dipartimento di Elettronica e Informazione, Milan, Italy
,
M. HonmaDivision of Genetics and Mutagenesis, National Institute of Health Sciences, Tokyo, Japan
,
M. KinzlUmweltbundesamt GmbH, Vienna, Austria
,
R. KnaufCentro REACH S.r.l., Milan, Italy
,
A. ManganaroKode S.r.l. Pisa, Italy
,
E. MombelliInstitut National de l’Environnement Industriel et des Risques, Verneuil-en-Halatte, France
,
M. I. PetoumenouIRCCS – Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
,
M. PaparellaUmweltbundesamt GmbH, Vienna, Austria
,
P. ParisIstituto Superiore per la Protezione e la Ricerca Ambientale, Rome, Italy
&
G. RaitanoIRCCS – Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
 show all
Pages 371-384 | Received 18 Mar 2016, Accepted 11 Apr 2016, Published online: 11 May 2016

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P. Kumar, A. Kumar & J. Sindhu. (2019) In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method. SAR and QSAR in Environmental Research 30:8, pages 525-541.
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Articles from other publishers (16)

Giuseppina Gini, Chiakang Hung & Emilio Benfenati. 2023. Big Data Analytics in Chemoinformatics and Bioinformatics. Big Data Analytics in Chemoinformatics and Bioinformatics 115 150 .
Giuseppina Gini. 2022. In Silico Methods for Predicting Drug Toxicity. In Silico Methods for Predicting Drug Toxicity 1 26 .
Giuseppina Gini. (2020) The QSAR similarity principle in the deep learning era: Confirmation or revision?. Foundations of Chemistry 22:3, pages 383-402.
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Emilio Benfenati & Anna Lombardo. 2020. Ecotoxicological QSARs. Ecotoxicological QSARs 759 787 .
George Helman, Imran Shah & Grace Patlewicz. (2019) Transitioning the generalised read-across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data. Computational Toxicology 12, pages 100097.
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Emilio Benfenati, Qasim Chaudhry, Giuseppina Gini & Jean Lou Dorne. (2019) Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy. Environment International 131, pages 105060.
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Grace Patlewicz, Lucina E. Lizarraga, Diego Rua, David G. Allen, Amber B. Daniel, Suzanne C. Fitzpatrick, Natàlia Garcia-Reyero, John Gordon, Pertti Hakkinen, Angela S. Howard, Agnes Karmaus, Joanna Matheson, Moiz Mumtaz, Andrea-Nicole Richarz, Patricia Ruiz, Louis Scarano, Takashi Yamada & Nicole Kleinstreuer. (2019) Exploring current read-across applications and needs among selected U.S. Federal Agencies. Regulatory Toxicology and Pharmacology 106, pages 197-209.
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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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Emilio Benfenati, Alessandra Roncaglioni, Anna Lombardo & Alberto Manganaro. 2019. Advances in Computational Toxicology. Advances in Computational Toxicology 365 381 .
Emilio Benfenati, Anna Lombardo & Alessandra Roncaglioni. 2018. Computational Toxicology. Computational Toxicology 245 268 .
Anna Lombardo, Giuseppa Raitano, Domenico Gadaleta & Emilio Benfenati. 2018. Computational Toxicology. Computational Toxicology 199 218 .
Grace Patlewicz, George Helman, Prachi Pradeep & Imran Shah. (2017) Navigating through the minefield of read-across tools: A review of in silico tools for grouping. Computational Toxicology 3, pages 1-18.
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Agnieszka Gajewicz. (2017) What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps. Nanoscale 9:24, pages 8435-8448.
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A. Gajewicz. (2017) Development of valuable predictive read-across models based on “real-life” (sparse) nanotoxicity data. Environmental Science: Nano 4:6, pages 1389-1403.
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A. Gajewicz, K. Jagiello, M. T. D. Cronin, J. Leszczynski & T. Puzyn. (2017) Addressing a bottle neck for regulation of nanomaterials: quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available. Environmental Science: Nano 4:2, pages 346-358.
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Azadi Golbamaki, Alessio Mauro Franchi & Giuseppina Gini. 2017. Advances in QSAR Modeling. Advances in QSAR Modeling 149 165 .

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