Predictive Carcinogenicity:  A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network

Giuseppina Gini; Marco Lorenzini; Emilio Benfenati; Paola Grasso; Maurizio Bruschi

doi:10.1021/ci9903096

Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network

Journal of Chemical Information and Computer Sciences ◽

10.1021/ci9903096 ◽

1999 ◽

Vol 39 (6) ◽

pp. 1076-1080 ◽

Cited By ~ 55

Author(s):

Giuseppina Gini ◽

Marco Lorenzini ◽

Emilio Benfenati ◽

Paola Grasso ◽

Maurizio Bruschi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Aromatic Compounds ◽

Molecular Descriptors ◽

Artificial Neural

ChemInform Abstract: Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network.

ChemInform ◽

10.1002/chin.200007234 ◽

2010 ◽

Vol 31 (7) ◽

pp. no-no

Author(s):

Giuseppina Gini ◽

Marco Lorenzini ◽

Emilio Benfenati ◽

Paola Grasso ◽

Maurizio Bruschi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Aromatic Compounds ◽

Molecular Descriptors ◽

Artificial Neural

Simultaneous Prediction of the Logarithmic Capacity Factor of Some Aliphatic and Aromatic Compounds on Five Different Stationary Phases in RP-LC Using Artificial Neural Network

Chromatographia ◽

10.1007/s10337-012-2257-x ◽

2012 ◽

Vol 75 (13-14) ◽

pp. 701-710 ◽

Cited By ~ 1

Author(s):

Zahra Dashtbozorgi ◽

Hassan Golmohammadi ◽

Elahe Konoz

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Aromatic Compounds ◽

Stationary Phases ◽

Logarithmic Capacity ◽

Capacity Factor ◽

Simultaneous Prediction ◽

Artificial Neural

"Artificial neural network designed to identify NBOMe hallucinogens based on molecular descriptors "

Annals of the ”Dunarea de Jos” University of Galati. Fascicle II, Mathematics, Physics, Theoretical Mechanics ◽

10.35219/ann-ugal-math-phys-mec.2018.1.05 ◽

2018 ◽

Vol 41 (1) ◽

pp. 33-39

Author(s):

Steluța Gosev ◽

◽

Adelina Ion ◽

Mirela Praisler ◽

◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Molecular Descriptors ◽

Artificial Neural

Modelling of Cytotoxicity Data (CC50) of Anti-HIV 1-[5-Chlorophenyl) Sulfonyl]-1H-Pyrrole Derivatives Using Calculated Molecular Descriptors and Levenberg-Marquardt Artificial Neural Network

Chemical Biology & Drug Design ◽

10.1111/j.1747-0285.2009.00790.x ◽

2009 ◽

Vol 73 (4) ◽

pp. 456-465 ◽

Cited By ~ 14

Author(s):

M. Arab Chamjangali

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Molecular Descriptors ◽

Pyrrole Derivatives ◽

Levenberg Marquardt ◽

Artificial Neural ◽

Anti Hiv ◽

Hiv 1

Chapter 4 Relevance of the Molecular Descriptors for the Modeling/Discrimination of Amphetamines Using Artificial Neural Network

Electromagnetic Radiation in Analysis and Design of Organic Materials ◽

10.1201/9781315164984-5 ◽

2017 ◽

pp. 59-74 ◽

Cited By ~ 2

Author(s):

Steluta Gosav

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Molecular Descriptors ◽

Artificial Neural

Development of a 3D QSPR model for adsorption of aromatic compounds by carbon nanotubes: comparison of multiple linear regression, artificial neural network and support vector machine

RSC Advances ◽

10.1039/c3ra43599g ◽

2013 ◽

Vol 3 (46) ◽

pp. 23924 ◽

Cited By ~ 17

Author(s):

Qiliang ‘Luke’ Wang ◽

Onur G. Apul ◽

Pengfei Xuan ◽

Feng Luo ◽

Tanju Karanfil

Keyword(s):

Neural Network ◽

Carbon Nanotubes ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Linear Regression ◽

Multiple Linear Regression ◽

Aromatic Compounds ◽

Support Vector ◽

Qspr Model ◽

Artificial Neural

Accurate prediction of standard enthalpy of formation based on semiempirical quantum chemistry methods with artificial neural network and molecular descriptors

10.22541/au.159135487.70823246 ◽

2020 ◽

Author(s):

zhongyu Wan ◽

Quan de Wang ◽

Jinhu Liang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Quantum Chemistry ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Molecular Descriptors ◽

Accurate Prediction ◽

Standard Enthalpy Of Formation ◽

Artificial Neural

Phase Equilibrium Involving Xylitol, Water, and Ethylene Glycol or 1,2-Propylene Glycol: Experimental Data, Activity Coefficient Modeling, and Prediction with Artificial Neural Network-Molecular Descriptors

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.8b02480 ◽

2018 ◽

Vol 57 (31) ◽

pp. 10675-10683 ◽

Cited By ~ 5

Author(s):

Patrícia G. Machado ◽

Alessandro C. Galvão ◽

Weber S. Robazza ◽

Pedro F. Arce ◽

Bruna E. Hochscheidt

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Phase Equilibrium ◽

Activity Coefficient ◽

Ethylene Glycol ◽

Propylene Glycol ◽

Molecular Descriptors ◽

Modeling And Prediction ◽

Artificial Neural

Comparison between the artificial neural network, SAFT and PRSV approach in obtaining the solubility of solid aromatic compounds in supercritical carbon dioxide

The Journal of Supercritical Fluids ◽

10.1016/j.supflu.2013.02.027 ◽

2013 ◽

Vol 77 ◽

pp. 44-51 ◽

Cited By ~ 49

Author(s):

B. Vaferi ◽

M. Karimi ◽

M. Azizi ◽

H. Esmaeili

Keyword(s):

Neural Network ◽

Carbon Dioxide ◽

Artificial Neural Network ◽

Supercritical Carbon Dioxide ◽

Aromatic Compounds ◽

Artificial Neural ◽

Supercritical Carbon

Aqueous viscosity of carbohydrates: Experimental data, activity coefficient modeling, and prediction with artificial neural network-molecular descriptors

Journal of Molecular Liquids ◽

10.1016/j.molliq.2020.114932 ◽

2021 ◽

Vol 322 ◽

pp. 114932

Author(s):

Karine Varnier ◽

Alessandro C. Galvão ◽

Pedro F. Arce ◽

Weber S. Robazza

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Activity Coefficient ◽

Molecular Descriptors ◽

Modeling And Prediction ◽

Artificial Neural