Quantitative and qualitative prediction of corneal permeability for drug-like compounds.

A set of 69 drug-like compounds with corneal permeability was studied using quantitative and qualitative modeling techniques. Multiple linear regression (MLR) and multilayer perceptron neural network (MLP-NN) were used to develop quantitative relationships between the corneal permeability and seven molecular descriptors selected by stepwise MLR and sensitivity analysis methods. In order to evaluate the models, a leave many out cross-validation test was performed, which produced the statistic Q(2)=0.

Classification of drugs according to their milk/plasma concentration ratio.

The classification of drugs was done according to their milk/plasma concentration ratio (M/P) by using counter propagation artificial neural network (CP-ANN). The features of each drug were encoded by linear free energy relationship (LFER) parameters. These descriptors were used as inputs for developing linear discriminant analysis, quadratic discriminant analysis, least square support vector machine and CP-ANN models to distinguish the potential risk of 154 drugs as high risk (with M/P > 1) and low risk (with M/P < 1) for lactating women.

In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set.