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Emilio Benfenati
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2020 – today
- 2024
- [j35]Domenico Gadaleta, Marina Garcia de Lomana, Eva Serrano-Candelas, Rita Ortega-Vallbona, Rafael Gozalbes, Alessandra Roncaglioni, Emilio Benfenati:
Quantitative structure-activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity. J. Cheminformatics 16(1): 122 (2024) - 2021
- [j34]Alla P. Toropova
, Andrey A. Toropov
The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella). Comput. Biol. Medicine 133: 104370 (2021)
2010 – 2019
- 2019
- [j33]Kristijan Vukovic
Methodology of aiQSAR: a group-specific approach to QSAR modelling. J. Cheminformatics 11(1): 27:1-27:9 (2019) - [j32]Domenico Gadaleta, Anna Lombardo, Cosimo Toma, Emilio Benfenati:
Correction to: A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications. J. Cheminformatics 11(1): 31:1 (2019) - [j31]Domenico Gadaleta
SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. J. Cheminformatics 11(1): 58:1-58:16 (2019) - 2018
- [j30]Alla P. Toropova
Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES. Biosyst. 169-170: 5-12 (2018) - [j29]Domenico Gadaleta, Anna Lombardo
A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications. J. Cheminformatics 10(1): 60:1-60:13 (2018) - [j28]Domenico Gadaleta, Serena Manganelli, Alessandra Roncaglioni
QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis. J. Chem. Inf. Model. 58(8): 1501-1517 (2018) - 2014
- [j27]Matteo Floris
A generalizable definition of chemical similarity for read-across. J. Cheminformatics 6(1): 39 (2014) - 2013
- [c14]Emilio Benfenati, Alberto Manganaro, Giuseppina C. Gini:
VEGA-QSAR: AI Inside a Platform for Predictive Toxicology. PAI@AI*IA 2013: 21-28 - 2012
- [j26]Alla P. Toropova
Coral: QSAR models for acute toxicity in fathead minnow (Pimephales promelas). J. Comput. Chem. 33(12): 1218-1223 (2012) - [j25]Andrey A. Toropov
Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical. J. Comput. Chem. 33(23): 1902-1906 (2012) - 2011
- [j24]Natalja Fjodorova, Marjana Novic, Alessandra Roncaglioni
Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network. J. Comput. Aided Mol. Des. 25(12): 1147-1158 (2011) - [j23]Alla P. Toropova
CORAL: Quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats. J. Comput. Chem. 32(12): 2727-2733 (2011) - [j22]Mosé Casalegno, Emilio Benfenati, Guido Sello:
Identification of Toxifying and Detoxifying Moieties for Mutagenicity Prediction by Priority Assessment. J. Chem. Inf. Model. 51(7): 1564-1574 (2011) - [c13]Thomas Ferrari, Giuseppina C. Gini, Nazanin Golbamaki Bakhtyari, Emilio Benfenati:
Mining toxicity structural alerts from SMILES: A new way to derive Structure Activity Relationships. CIDM 2011: 120-127 - 2010
- [j21]Andrey A. Toropov
SMILES-based optimal descriptors: QSAR analysis of fullerene-based HIV-1 PR inhibitors by means of balance of correlations. J. Comput. Chem. 31(2): 381-392 (2010)
2000 – 2009
- 2009
- [j20]Andrey A. Toropov
QSPR modeling of enthalpies of formation for organometallic compounds by SMART-based optimal descriptors. J. Comput. Chem. 30(15): 2576-2582 (2009) - 2008
- [j19]Mosé Casalegno
Definition and Detection of Outliers in Chemical Space. J. Chem. Inf. Model. 48(8): 1592-1601 (2008) - 2007
- [j18]Andrey A. Toropov
SMILES as an alternative to the graph in QSAR modelling of bee toxicity. Comput. Biol. Chem. 31(1): 57-60 (2007) - [j17]Giuseppina C. Gini
E-Modelling: Foundations and Cases for Applying AI to Life Sciences. Int. J. Artif. Intell. Tools 16(2): 243-268 (2007) - [p1]Mathilde Romberg, Emilio Benfenati, Werner Dubitzky:
Open Computing Grid for Molecular Sciences. Grid Computing for Bioinformatics and Computational Biology 2007: 1-21 - 2006
- [j16]Frank Lemke, Emilio Benfenati, Johann-Adolf Müller:
Data-driven modeling and prediction of acute toxicity of pesticide residues. SIGKDD Explor. 8(1): 71-79 (2006) - [c12]Uko Maran, Sulev Sild, Paolo Mazzatorta, Mosé Casalegno, Emilio Benfenati, Mathilde Romberg:
Grid Computing for the Estimation of Toxicity: Acute Toxicity on Fathead Minnow (Pimephales promelas). GCCB 2006: 60-74 - 2005
- [j15]Tatiana I. Netzeva, Aynur O. Aptula
Description of the Electronic Structure of Organic Chemicals Using Semiempirical and Ab Initio Methods for Development of Toxicological QSARs. J. Chem. Inf. Model. 45(1): 106-114 (2005) - [j14]Martin Smiesko
Thermodynamic Descriptors Derived from Density Functional Theory Calculations in Prediction of Aquatic Toxicity. J. Chem. Inf. Model. 45(2): 379-385 (2005) - [j13]Filip Fratev
3D-QSAR and Molecular Mechanics Study for the Differences in the Azole Activity against Yeastlike and Filamentous Fungi and Their Relation to P450DM Inhibition. 1. 3-Substituted-4(3H)-quinazolinones. J. Chem. Inf. Model. 45(3): 634-644 (2005) - [j12]Paolo Mazzatorta, Martin Smiesko
QSAR Model for Predicting Pesticide Aquatic Toxicity. J. Chem. Inf. Model. 45(6): 1767-1774 (2005) - [c11]Sulev Sild, Uko Maran, Mathilde Romberg, Bernd Schuller
OpenMolGRID: Using Automated Workflows in GRID Computing Environment. EGC 2005: 464-473 - 2004
- [j11]Christoph König, Giuseppina C. Gini
Multiclass Classifier From A Combination Of Local Experts: Toward Distributed Computation For Real-Problem Classifiers. Int. J. Pattern Recognit. Artif. Intell. 18(5): 801-817 (2004) - [j10]Paolo Mazzatorta, Emilio Benfenati, Paola Lorenzini, Marco Vighi:
QSAR in Ecotoxicity: An Overview of Modern Classification Techniques. J. Chem. Inf. Model. 44(1): 105-112 (2004) - [j9]Alessandra Roncaglioni
Classification of Potential Endocrine Disrupters on the Basis of Molecular Structure Using a Nonlinear Modeling Method. J. Chem. Inf. Model. 44(2): 300-309 (2004) - [j8]Martin Smiesko
Predictive Models for Aquatic Toxicity of Aldehydes Designed for Various Model Chemistries. J. Chem. Inf. Model. 44(3): 976-984 (2004) - [j7]Giuseppina C. Gini, Marian Viorel Craciun, Christoph König, Emilio Benfenati:
Combining Unsupervised and Supervised Artificial Neural Networks to PredictAquatic Toxicity. J. Chem. Inf. Model. 44(6): 1897-1902 (2004) - [c10]Frank Lemke, Johann-Adolf Müller, Emilio Benfenati:
Modelling and Prediction of Toxicity of Environmental Pollutants. KELSI 2004: 221-234 - [c9]Emilio Benfenati:
Modelling Aquatic Toxicity with Advanced Computational Techniques: Procedures to Standardize Data and Compare Models. KELSI 2004: 235-248 - [c8]Paolo Mazzatorta, Emilio Benfenati, Bernd Schuller, Mathilde Romberg, Damian McCourt, Werner Dubitzky, Sulev Sild, Mati Karelson, Ákos Papp, István Bágyi, Ferenc Darvas:
OpenMolGRIND: Molecular Science and Engineering in a Grid Context. PDPTA 2004: 775-779 - [e1]Jesús A. López, Emilio Benfenati, Werner Dubitzky:
Knowledge Exploration in Life Science Informatics, International Symposium,KELSI 2004, Milan, Italy, November 25-26, 2004, Proceedings. Lecture Notes in Computer Science 3303, Springer 2004, ISBN 3-540-23927-8 [contents] - 2003
- [j6]Paolo Mazzatorta, Marjan Vracko, Emilio Benfenati:
ANVAS: Artificial Neural Variables Adaptation System for descriptor selection. J. Comput. Aided Mol. Des. 17(5-6): 335-346 (2003) - [j5]Paolo Mazzatorta, Marjan Vracko, Aneta Jezierska
Modeling Toxicity by Using Supervised Kohonen Neural Networks. J. Chem. Inf. Comput. Sci. 43(2): 485-492 (2003) - [j4]Paolo Mazzatorta, Emilio Benfenati, Ciprian-Daniel Neagu
Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling. J. Chem. Inf. Comput. Sci. 43(2): 513-518 (2003) - [c7]Damian McCourt, Jesús A. López, Emilio Benfenati, Paolo Mazzatorta, Mathilde Romberg, Bernd Schuller, Werner Dubitzky:
Towards and Intelligent Data Type for Toxicity. IC-AI 2003: 328-334 - 2002
- [j3]Paolo Mazzatorta, Emilio Benfenati, Daniel Neagu
The Importance of Scaling in Data Mining for Toxicity Prediction. J. Chem. Inf. Comput. Sci. 42(5): 1250-1255 (2002) - [c6]Ciprian-Daniel Neagu, Emilio Benfenati, Giuseppina C. Gini, Paolo Mazzatorta, Alessandra Roncaglioni:
Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds. ECAI 2002: 498-502 - [c5]Emilio Benfenati, Paolo Mazzatorta, Daniel Neagu
Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach. Multiple Classifier Systems 2002: 293-303 - [c4]Giuseppina C. Gini, Emilio Benfenati:
Results from a Data Mining Approach to Predictive Toxicology. The Case of Pesticides Data. PRIS 2002: 114-123 - 2001
- [j2]Alan R. Katritzky, Ruslan Petrukhin, Douglas B. Tatham, Subhash C. Basak, Emilio Benfenati, Mati Karelson, Uko Maran
Interpretation of Quantitative Structure-Property and -Activity Relationships. J. Chem. Inf. Comput. Sci. 41(3): 679-685 (2001) - [c3]Giuseppina C. Gini
Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds. Multiple Classifier Systems 2001: 126-135 - 2000
- [c2]Giuseppina C. Gini
Clustering and classification techniques to assess aquatic toxicity. KES 2000: 166-172
1990 – 1999
- 1999
- [j1]Giuseppina C. Gini, Marco Lorenzini, Emilio Benfenati, Paola Grasso, Maurizio Bruschi:
Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network. J. Chem. Inf. Comput. Sci. 39(6): 1076-1080 (1999) - [c1]Giuseppina C. Gini, Emilio Benfenati, Paola Grasso, Marco Lorenzini:
Predictive Toxicology Results Integrating Different AI Paradigms: The Carcinogenicity Case. IIA/SOCO 1999
Coauthor Index
aka: Giuseppina C. Gini
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