QSAR study of the enantiomeric excess in asymmetric catalytic reactions with topological indices and an artificial neural network

2006 ◽  
Vol 13 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Chen Jiang ◽  
Daliang Li ◽  
Jiwu Wen ◽  
Tianpa You
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Enantiomeric Excess ◽  
Topological Indices ◽  
Catalytic Reactions ◽  
Qsar Study ◽  
Asymmetric Catalytic ◽  
Artificial Neural

scholarly journals Kajian HKSA Antimalaria Senyawa Turunan Quinolon-4(1H)-imines Menggunakan Metode MLR-ANN

2019 ◽  
Vol 1 (1) ◽  
pp. 21-26
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Qsar Model ◽  
Ann Model ◽  
Multilinear Regression ◽  
Qsar Study ◽  
Structure Activity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Quantitatif Structure-Activity Relationship (QSAR) study of 22 antimalarial compounds of Quinolon-4(1H)-imines derivatives has been done using multilinear regression (MLR) and artificial neural network (ANN) methods. The best QSAR model was obtained from ANN analysis indicated by its higher correlation coefficient (r2) compared to MLR method, i.e. 0.931 with most influential descriptors is qC1, qC5, qC11, qN14 and log P.Keywords: Quinolon-4(1H)-imines, Antimalarial, QSAR, MLR-ANNTelah dilakukan kajian analisis Hubungan Kuantitatif Struktur Aktivitas (HKSA) terhadap 22 senyawa antimalaria turunan Quinolon-4(1H)-imines menggunakan metode regresi multilinear (MLR) dan artificial neural network (ANN). Model HKSA terbaik diperoleh dari hasil analisis menggunakan metode ANN yang ditunjukkan oleh nilai koefisien korelasi (r2) paling tinggi dibandingkan dengan metode MLR yaitu sebesar 0,931 dengan deskriptor paling berpengaruh terhadap aktivitas antimalaria turunan Quinolon-4(1H)-imines, yaitu qC1, qC5, qC11, qN14 dan log P.Kata Kunci: Quinolon-4(1H)-imines, Antimalaria, HKSA, MLR-ANN

scholarly journals QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

2014 ◽  
Vol 14 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Adi Syahputra ◽  
Mudasir Mudasir ◽  
Nuryono Nuryono ◽  
Anifuddin Aziz ◽  
Iqmal Tahir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Latent Variables ◽  
Statistical Technique ◽  
Pcr Analysis ◽  
Qsar Study ◽  
Principle Component ◽  
Artificial Neural

Quantitative structure activity relationship (QSAR) for 21 insecticides of phthalamides containing hydrazone (PCH) was studied using multiple linear regression (MLR), principle component regression (PCR) and artificial neural network (ANN). Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA) were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84). Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl)-5-chloro-N’-((5-methylthiophen-2-yl)methylene) benzohydrazide, 2-(decalinecarbamoyl)-5-chloro-N’-((thiophen-2-yl)-methylene) benzohydrazide and 2-(decaline carbamoyl)-N’-(4-fluorobenzylidene)-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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