Quantitative structure-property relationship modeling of water-to-wet butyl acetate partition coefficient of 76 organic solutes using multiple linear regression and artificial neural network

2010 ◽  
Vol 33 (23-24) ◽  
pp. 3800-3810 ◽  
Author(s):  
Zahra Dashtbozorgi ◽  
Hassan Golmohammadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Partition Coefficient ◽  
Multiple Linear Regression ◽  
Butyl Acetate ◽  
Organic Solutes ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Property Relationship

scholarly journals Investigation of Antileishmanial Activities of Acridines Derivatives against Promastigotes and Amastigotes Form of Parasites Using Quantitative Structure Activity Relationship Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Samir Chtita ◽  
Mounir Ghamali ◽  
Rachid Hmamouchi ◽  
Bouhya Elidrissi ◽  
Mohamed Bourass ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Artificial Neural Network Ann ◽  
Antileishmanial Activities ◽  
New Compounds

In a search of newer and potent antileishmanial (against promastigotes and amastigotes form of parasites) drug, a series of 60 variously substituted acridines derivatives were subjected to a quantitative structure activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds by using multiple linear regression and artificial neural network (ANN) methods. The used descriptors were computed with Gaussian 03, ACD/ChemSketch, Marvin Sketch, and ChemOffice programs. The QSAR models developed were validated according to the principles set up by the Organisation for Economic Co-operation and Development (OECD). The principal component analysis (PCA) has been used to select descriptors that show a high correlation with activities. The univariate partitioning (UP) method was used to divide the dataset into training and test sets. The multiple linear regression (MLR) method showed a correlation coefficient of 0.850 and 0.814 for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. Internal and external validations were used to determine the statistical quality of QSAR of the two MLR models. The artificial neural network (ANN) method, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.933 and 0.918 with 7-3-1 and 6-3-1 ANN models architecture for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outsides compounds. The effects of different descriptors in the activities were described and used to study and design new compounds with higher activities compared to the existing ones.

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