Alberto Manganaro
Alberto Manganaro
Kode Chemoinformatics srl
Email verificata su - Home page
Citata da
Citata da
CAESAR models for developmental toxicity
A Cassano, A Manganaro, T Martin, D Young, N Piclin, M Pintore, ...
Chemistry Central Journal 4 (1), 1-11, 2010
VEGA-QSAR: AI inside a platform for predictive toxicology.
E Benfenati, A Manganaro, GC Gini
PAI@ AI* IA, 21-28, 2013
Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction
T Ferrari, D Cattaneo, G Gini, N Golbamaki Bakhtyari, A Manganaro, ...
SAR and QSAR in Environmental Research 24 (5), 365-383, 2013
A generalizable definition of chemical similarity for read-across
M Floris, A Manganaro, O Nicolotti, R Medda, GF Mangiatordi, E Benfenati
Journal of cheminformatics 6 (1), 1-7, 2014
Comparison of in silico tools for evaluating rat oral acute toxicity
R Gonella Diaza, S Manganelli, A Esposito, A Roncaglioni, A Manganaro, ...
SAR and QSAR in Environmental Research 26 (1), 1-27, 2015
ToxRead: a tool to assist in read across and its use to assess mutagenicity of chemicals
G Gini, AM Franchi, A Manganaro, A Golbamaki, E Benfenati
SAR and QSAR in Environmental Research 25 (12), 999-1011, 2014
coral Software: QSAR for Anticancer Agents
E Benfenati, AA Toropov, AP Toropova, A Manganaro, R Gonella Diaza
Chemical biology & drug design 77 (6), 471-476, 2011
In silico models for predicting ready biodegradability under REACH: a comparative study
F Pizzo, A Lombardo, A Manganaro, E Benfenati
Science of the total environment 463, 161-168, 2013
Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm
A Manganaro, F Pizzo, A Lombardo, A Pogliaghi, E Benfenati
Chemosphere 144, 1624-1630, 2016
A new in silico classification model for ready biodegradability, based on molecular fragments
A Lombardo, F Pizzo, E Benfenati, A Manganaro, T Ferrari, G Gini
Chemosphere 108, 10-16, 2014
A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds
D Gadaleta, S Manganelli, A Manganaro, N Porta, E Benfenati
Toxicology 370, 20-30, 2016
Using toxicological evidence from QSAR models in practice
E Benfenati, S Pardoe, T Martin, RG Diaza, A Lombardo, A Manganaro, ...
ALTEX-Alternatives to animal experimentation 30 (1), 19-40, 2013
New quantitative structure–activity relationship models improve predictability of Ames mutagenicity for aromatic azo compounds
S Manganelli, E Benfenati, A Manganaro, S Kulkarni, TS Barton-Maclaren, ...
Toxicological Sciences 153 (2), 316-326, 2016
Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications
R Todeschini, D Ballabio, V Consonni, A Manganaro, A Mauri
Analytica chimica acta 648 (1), 45-51, 2009
Integrated in silico strategy for PBT assessment and prioritization under REACH
F Pizzo, A Lombardo, A Manganaro, CI Cappelli, MI Petoumenou, ...
Environmental Research 151, 478-492, 2016
Integrating in silico models to enhance predictivity for developmental toxicity
M Marzo, S Kulkarni, A Manganaro, A Roncaglioni, S Wu, ...
Toxicology 370, 127-137, 2016
QSAR modelling of carcinogenicity by balance of correlations
AA Toropov, AP Toropova, E Benfenati, A Manganaro
Molecular Diversity 13 (3), 367-373, 2009
Results of a round-robin exercise on read-across
E Benfenati, M Belli, T Borges, E Casimiro, J Cester, A Fernandez, G Gini, ...
SAR and QSAR in Environmental Research 27 (5), 371-384, 2016
Introduction to MOLE DB-on-line molecular descriptors database
D Ballabio, A Manganaro, V Consonni, A Mauri, R Todeschini
MATCH Commun Math Comput Chem 62, 199-207, 2009
A new structure-activity relationship (SAR) model for predicting drug-induced liver injury, based on statistical and expert-based structural alerts
F Pizzo, A Lombardo, A Manganaro, E Benfenati
Frontiers in pharmacology 7, 442, 2016
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
Articoli 1–20