A. Roncaglioni

发表

Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish

Emilio Benfenati, Anna Lombardo, Alessandra Roncaglioni, 2010, Chemistry Central journal.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

Ruili Huang, Igor V. Tetko, Eva Bay Wedebye, 2016, Environmental health perspectives.

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.

A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH.

Emilio Benfenati, Silke Gabbert, Ralph Kühne, 2013, Regulatory toxicology and pharmacology : RTP.

QSAR Development for Plasma Protein Binding: Influence of the Ionization State

E. Benfenati, A. Toropova, A. Toropov, 2018, Pharmaceutical Research.

ehp CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

Ruili Huang, Igor V. Tetko, Eva Bay Wedebye, 2016 .

Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across.

Emilio Benfenati, Alessandra Roncaglioni, Domenico Gadaleta, 2020, ALTEX.

Maintenance, update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0

Chihae Yang, E. Benfenati, S. Kovarich, 2022, EFSA Supporting Publications.

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.

Development, validation and integration of (Q)SAR models to identify androgen active chemicals

R. Judson, K. Mansouri, E. Benfenati, 2019 .

QSAR Methods to Screen Endocrine Disruptors

E. Benfenati, A. Roncaglioni, M. Marzo, 2016 .

In silico-aided prediction of biological properties of chemicals: oestrogen receptor-mediated effects.

E. Benfenati, A. Roncaglioni, 2008, Chemical Society reviews.

Maintenance, update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0

Chihae Yang, E. Benfenati, L. Ceriani, 2020, EFSA Supporting Publications.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

E. Benfenati, M. Saviano, G. Mangiatordi, 2022, Frontiers in Pharmacology.

Automated Integration of Structural, Biological and Metabolic Similarities to Improve Read-Across

Azadi Golbamaki Bakhtyari, E. Benfenati, Domenico Gadaleta, 2020 .

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods.

Agnes L. Karmaus, David C. Jones, G. Myatt, 2022, Computational toxicology.

Human-Based New Approach Methodologies in Developmental Toxicity Testing: A Step Ahead from the State of the Art with a Feto–Placental Organ-on-Chip Platform

E. Benfenati, M. Sogorb, T. May, 2022, International journal of environmental research and public health.

Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation.

Emilio Benfenati, Alessandra Roncaglioni, Edoardo Carnesecchi, 2020, The Science of the total environment.

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

Emilio Benfenati, Giovanna J. Lavado, Alessandra Roncaglioni, 2019, Journal of Cheminformatics.

Global QSAR models of skin sensitisers for regulatory purposes

Marco Pintore, Nadège Piclin, Qasim Chaudhry, 2010, Chemistry Central journal.

Development, validation and integration of in silico models to identify androgen active chemicals.

Emilio Benfenati, Patricia Ruiz, Alberto Manganaro, 2019, Chemosphere.

CATMoS: Collaborative Acute Toxicity Modeling Suite

Kimberley M. Zorn, Daniel P. Russo, Timothy E. H. Allen, 2021, Environmental health perspectives.

Prediction of the Neurotoxic Potential of Chemicals Based on Modelling of Molecular Initiating Events Upstream of the Adverse Outcome Pathways of (Developmental) Neurotoxicity

M. Cronin, E. Benfenati, Domenico Gadaleta, 2022, International journal of molecular sciences.

Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF).

Emilio Benfenati, Anna Lombardo, Alessandra Roncaglioni, 2015, Environmental research.

Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds

Giuseppina C. Gini, Emilio Benfenati, Daniel Neagu, 2002, ECAI.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

Ruili Huang, Scott Boyer, Xin Hu, 2020, Environmental health perspectives.

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF).

Emilio Benfenati, Alessandra Roncaglioni, Elena Boriani, 2008, Chemosphere.

Toxicology of 3-monochloropropane-1,2-diol and its esters: a narrative review

E. Davoli, E. Fattore, R. Bagnati, 2023, Archives of Toxicology.

A KNIME Workflow to Assist the Analogue Identification for Read-Across, Applied to Aromatase Activity

E. Benfenati, Domenico Gadaleta, A. Roncaglioni, 2023, Molecules.

Quantitative Structure-Activity Relationship Modeling of the Amplex Ultrared Assay to Predict Thyroperoxidase Inhibitory Activity

E. Benfenati, Domenico Gadaleta, A. Roncaglioni, 2021, Frontiers in Pharmacology.

QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis

Emilio Benfenati, Enrico Mombelli, Serena Manganelli, 2018, J. Chem. Inf. Model..

CORAL Models for Drug-Induced Nephrotoxicity

E. Benfenati, R. Masereeuw, A. Toropova, 2023, Toxics.

In Silico Methods for Carcinogenicity Assessment.

E. Benfenati, A. Roncaglioni, A. Golbamaki, 2022, Methods in molecular biology.

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds

Emilio Benfenati, Giuseppina Gini, Alberto Manganaro, 2016, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

New public QSAR model for carcinogenicity

Emilio Benfenati, Alessandra Roncaglioni, Natalja Fjodorova, 2010, Chemistry Central journal.

A LIFE-EDESIA project update: An animal-free approach to search for SVHC alternatives

A. Mantovani, E. Benfenati, E. Perissutti, 2015 .

The role of in silico tools in supporting the application of the substitution principle.

Emilio Benfenati, Alessandra Roncaglioni, Alberto Mantovani, 2015, ALTEX.

CORAL: building up the model for bioconcentration factor and defining it's applicability domain.

E Benfenati, G Gini, A P Toropova, 2011, European journal of medicinal chemistry.

Computer-aided methodologies to predict endocrine-disrupting potency of chemicals

E. Benfenati, A. Roncaglioni, E. Benfenati, 2009 .

Validation of the models

Marco Pintore, Nadège Piclin, Emilio Benfenati, 2007 .

Quantitative consensus of bioaccumulation models for integrated testing strategies.

Emilio Benfenati, Anna Lombardo, Robert Rallo, 2012, Environment international.

CORAL: the prediction of biodegradation of organic compounds with optimal SMILES-based descriptors

Emilio Benfenati, Andrey A. Toropov, Alla P. Toropova, 2012 .

Integrated in silico strategy for PBT assessment and prioritization under REACH.

Emilio Benfenati, Anna Lombardo, Alberto Manganaro, 2016, Environmental research.

The system of self-consistent models for vapour pressure

E. Benfenati, A. Toropova, A. Toropov, 2022, Chemical Physics Letters.

The enhancement scheme for the predictive ability of QSAR: A case of mutagenicity.

E. Benfenati, A. Toropova, A. Toropov, 2023, Toxicology in vitro : an international journal published in association with BIBRA.

Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results.

Emilio Benfenati, Alessandra Roncaglioni, Andrey A Toropov, 2018, Methods in molecular biology.

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.

The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella)

Emilio Benfenati, Andrey A. Toropov, Alla P. Toropova, 2021, Comput. Biol. Medicine.

A comparison of DEMETRA individual QSARs with an index for evaluation of uncertainty.

Emilio Benfenati, Alessandra Roncaglioni, Antonio Chana, 2008, Chemosphere.

SpheraCosmolife: a new tool for the risk assessment of cosmetic products.

E. Benfenati, D. Baderna, A. Manganaro, 2021, ALTEX.

The VEGAHUB Platform: The Philosophy and the Tools

E. Benfenati, A. Lombardo, A. Roncaglioni, 2022, Alternatives to laboratory animals : ATLA.

Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite

Stephen P. Boyd, Kimberley M. Zorn, Daniel P. Russo, 2021, Environmental health perspectives.

Data quality assessment for in silico methods: A survey of approaches and needs

Helmut Segner, Judith C. Madden, Silke Gabbert, 2010 .

Comparison of in silico tools for evaluating rat oral acute toxicity†

E. Benfenati, E. Benfenati, A. Manganaro, 2015, SAR and QSAR in environmental research.

Identification of the structural requirements of the receptor-binding affinity of diphenolic azoles to estrogen receptors alpha and beta by three-dimensional quantitative structure-activity relationship and structure-activity relationship analysis.

Emilio Benfenati, Alessandra Roncaglioni, Mark T D Cronin, 2005, Journal of medicinal chemistry.

The system of self-consistent models for pesticide toxicity to Daphnia magna

E. Benfenati, A. Toropova, A. Toropov, 2023, Toxicology mechanisms and methods.

The system of self-consistent semi-correlations as one of the tools of cheminformatics for design antiviral drugs

E. Benfenati, A. Toropova, A. Toropov, 2021, New Journal of Chemistry.

The validation of predictive potential via the system of self-consistent models: the simulation of blood–brain barrier permeation of organic compounds

E. Benfenati, J. Leszczynski, D. Leszczyńska, 2023, Journal of Molecular Modeling.

Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells

J. C. Gómez-Tamayo, G. V. van Westen, U. Norinder, 2023, Journal of Chemical Information and Modeling.

In silico methods to predict drug toxicity.

Emilio Benfenati, Alessandra Roncaglioni, Andrey A Toropov, 2013, Current opinion in pharmacology.

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.

Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures

E. Benfenati, N. Piclin, M. Pintore, 2006, SAR and QSAR in environmental research.

Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network

Emilio Benfenati, Marjana Novic, Alessandra Roncaglioni, 2011, J. Comput. Aided Mol. Des..

Variable Selection and Interpretation in Structure-Affinity Correlation Modeling of Estrogen Receptor Binders

Marjana Novic, Federico Marini, Alessandra Roncaglioni, 2005, J. Chem. Inf. Model..

The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity

E. Benfenati, A. Toropova, A. Toropov, 2023, Toxics.

Binary classification models for endocrine disrupter effects mediated through the estrogen receptor

E Benfenati, E. Benfenati, N. Piclin, 2008, SAR and QSAR in environmental research.

Classification of Potential Endocrine Disrupters on the Basis of Molecular Structure Using a Nonlinear Modeling Method

Emilio Benfenati, Marjana Novic, Marjan Vracko, 2004, J. Chem. Inf. Model..

Prediction of Endocrine-Disrupting Chemicals Related to Estrogen, Androgen, and Thyroid Hormone (EAT) Modalities Using Transcriptomics Data and Machine Learning

A. Roncaglioni, M. Marzo, Emilio Benfenati, 2024, Toxics.

Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions.

G. Myatt, K. Cross, R. Benigni, 2021, Computational toxicology.