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Research Article

Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones

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Pages 980-995 | Received 19 Sep 2007, Accepted 29 Oct 2007, Published online: 20 Oct 2008

Figures & data

Table I. Structural features, observed and calculated data of HIV-1 reverse transcriptase inhibitory activity of indole aryl sulfones.

Table II. Values of physicochemical parameters (substituent constants)#.

Table III. Definition of indicator variables.

Table IV. Different equations obtained from topological descriptors using different statistical methods (whole set).

Table V. Different equations obtained from combined set of descriptors using different statistical methods (whole set).

Table VI. Comparative study of statistical parameters of models using different descriptors.

Table VII. Serial numbers of compounds under different clusters.

Table VIII. Different equations obtained from combined set of descriptors using different statistical methods from the training set.

Table IX. Comparison of predictivity parameters of different models obtained from the training set.

Table X. Comparative study of different networks.

Table XI. Comparison of external predictivity characteristics of different ANN models.

Table XII. Comparison of r2 between observed and predicted values of the test set compounds using different techniques.

Figure 1. Scatter plots of observed versus predicted values of the test set compounds using (a) stepwise regression model {Equation 16]; (b) GFA-MLR model [Equation 19; (c) artificial neural network model [ANN model 4].

Figure 1.  Scatter plots of observed versus predicted values of the test set compounds using (a) stepwise regression model {Equation 16]; (b) GFA-MLR model [Equation 19; (c) artificial neural network model [ANN model 4].

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