G. Raitano

发表

In silico tools and transcriptomics analyses in the mutagenicity assessment of cosmetic ingredients: a proof-of-principle on how to add weight to the evidence.

Emilio Benfenati, Dimiter Milushev, Vera Rogiers, 2016, Mutagenesis.

New in silico models to predict in vitro micronucleus induction as marker of genotoxicity.

E. Benfenati, D. Baderna, A. Lombardo, 2019, Journal of hazardous materials.

In silico model for mutagenicity (Ames test), taking into account metabolism

E. Benfenati, A. Roncaglioni, G. Raitano, 2019, Mutagenesis.

QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors

E. Benfenati, N. Kramer, D. Baderna, 2020, Molecules.

(Eco)toxicological maps: A new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy).

E. Benfenati, E. Aneggi, D. Goi, 2018, Environment international.

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

E. Benfenati, J. Dorne, A. Manganaro, 2022, Molecules.

CORAL: Building up QSAR models for the chromosome aberration test

Emilio Benfenati, Andrey A. Toropov, Alla P. Toropova, 2018, Saudi journal of biological sciences.

(Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances.

Emilio Benfenati, Vera Rogiers, Serena Manganelli, 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA’s databases

E. Benfenati, A. Gamba, A. Roncaglioni, 2020, SAR and QSAR in environmental research.

New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test.

Emilio Benfenati, Vera Rogiers, Masamitsu Honma, 2020, Toxicology letters.

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

E. Benfenati, E. Mombelli, G. Raitano, 2022, Methods in molecular biology.

Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project

Alessandro Giuliani, Emilio Benfenati, Ulf Norinder, 2018, Mutagenesis.

A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity$

E Benfenati, G Gini, Elena Lo Piparo, 2018, SAR and QSAR in environmental research.

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

E Benfenati, G Raitano, E. Benfenati, 2015, SAR and QSAR in environmental research.

In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results.

Emilio Benfenati, Enrico Mombelli, Giuseppa Raitano, 2016, Methods in molecular biology.

Hierarchical Rules for Read-Across and In Silico Models of Mutagenicity

Emilio Benfenati, Sabrina Giordano, Alberto Manganaro, 2015, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

Comparison of In Silico Models for Prediction of Mutagenicity

Emilio Benfenati, Todd Martin, Douglas Young, 2013, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

Evaluation of QSAR Models for the Prediction of Ames Genotoxicity: A Retrospective Exercise on the Chemical Substances Registered Under the EU REACH Regulation

Emilio Benfenati, Enrico Mombelli, Alessandra Roncaglioni, 2014, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

New approach methodologies to facilitate and improve the hazard assessment of non-genotoxic carcinogens—a PARC project

X. Coumoul, E. Benfenati, B. van de Water, 2023, Frontiers in toxicology.

Could deep learning in neural networks improve the QSAR models?

E Benfenati, G Raitano, G Gini, 2019, SAR and QSAR in environmental research.

Integrating in silico models for the prediction of mutagenicity (Ames test) of botanical ingredients of cosmetics

Alberto Manganaro, Alessandra Roncaglioni, Masamitsu Honma, 2019, Computational Toxicology.

In silico tools and transcriptomics analyses in the mutagenicity assessment of cosmetic ingredients: a proof-of-principle on how to add weight to the evidence.

V. Rogiers, E. Benfenati, G. Ateş, 2016, Mutagenesis.