A QSAR study of antiplatelet agents using artificial neural network - correlation with micelle-water partition coefficient

1997 ◽  
Vol 7 (7) ◽  
pp. 877-880 ◽  
Author(s):  
Nanda Ghoshal ◽  
Basudeb Achari ◽  
Tapan K. Ghoshal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Partition Coefficient ◽  
Antiplatelet Agents ◽  
Qsar Study ◽  
Water Partition Coefficient ◽  
Artificial Neural

QSPR Studies on n-Octanol/Water Partition Coefficient of Polychlorinated Biphenyls by Using Artificial Neural Network

2012 ◽  
Vol 455-456 ◽  
pp. 925-929
Author(s):  
Long Jiao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polychlorinated Biphenyls ◽  
Partition Coefficient ◽  
Quantitative Relationship ◽  
Calibration Model ◽  
Structure Property ◽  
Qspr Model ◽  
Water Partition Coefficient ◽  
Artificial Neural

Quantitative structure property relationship (QSPR) model for predicting the n-octanol/water partition coefficient, Kow, of 21 polychlorinated biphenyls (PCBs) was investigated. The structure of the investigated PCBs is mathematically characterized by using molecular distance-edge vector (MDEV) index, a topological index which is developed based on the topological method. The calibration model of Kow was developed by using radial basis function artificial neural network (RBF ANN). Leave one out cross validation was carried out to assess the predictive ability of the developed QSPR model. The R2 between the predicted and experimental logKow is 0.9793. The prediction RMS%RE for the 21 PCBs is 1.92. It is demonstrated that there is a quantitative relationship between the MDEV index and the Kow of the 21 PCBs. RBF ANN is shown to practicable for developing the QSPR model for Kow of PCBs.

QSPR Study on the Soil-Water Partition Coefficient of Polychlorinated Biphenyls by Using Artificial Neural Network

2012 ◽  
Vol 455-456 ◽  
pp. 930-934 ◽  
Author(s):  
Long Jiao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polychlorinated Biphenyls ◽  
Partition Coefficient ◽  
Soil Water ◽  
Calibration Model ◽  
Qspr Model ◽  
Structural Descriptors ◽  
Water Partition Coefficient ◽  
Artificial Neural

A practicable quantitative structure property relationship (QSPR) model for predicting the soil-water partition coefficient, Koc, of 16 polychlorinated biphenyls (PCBs) was developed. The structure of the investigated PCBs is encoded by five quantum structural descriptors and on topological index. The calibration model of Koc was developed by using artificial neural network (ANN). The input variables of ANN were generated from 6 structural descriptors by using principal component analysis (PCA). Leave one out cross validation was carried out to assess the predictive ability of the developed model. The prediction RMS%RE for the 16 PCBs is 6.35. The R2 between the predicted and experimental logKoc is 0.8522. It is demonstrated that ANN combined with PCA is a practicable method for developing QSPR model for Koc of these PCBs.

Partition Coefficient Prediction of a Large Set of Various Drugs and Poisons by a Genetic Algorithm and Artificial Neural Network

2008 ◽  
Vol 55 (2) ◽  
pp. 345-355 ◽  
Author(s):  
Siavash Riahi ◽  
Abolghasem Beheshti ◽  
Ali Mohammadi ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Partition Coefficient ◽  
Large Set ◽  
Artificial Neural
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