A QSAR study of some cyclobutenediones as CCR1 antagonists by artificial neural networks based on principal component analysis.

BACKGROUND AND PURPOSE OF THE STUDY: A quantitative structure activity relationship (QSAR) model based on artificial neural networks (ANN) was developed to study the activities of 29 derivatives of 3-amino-4-(2-(2-(4-benzylpiperazin-1-yl)-2-oxoethoxy) phenylamino) cyclobutenedione as C-C chemokine receptor type 1(CCR1) inhibitors. METHODS: A feed-forward ANN with error back-propagation learning algorithm was used for model building which was achieved by optimizing initial learning rate, learning momentum, epoch and the number of hidden neurons. RESULTS: Good results were obtained with a Root Mean Square Error (RMSE) and correlation coefficients (R(2)) of 0.189 and 0.906 for the training and 0.103 and 0.932 prediction sets, respectively. CONCLUSION: The results reflect a nonlinear relationship between the Principal components obtained from calculated molecular descriptors and the inhibitory activities of the investigated molecules.

A modeling study of aldehyde inhibitors of human cathepsin K using partial least squares method.

Quantitative relationships between molecular structure of forty eight aldehyde compounds with their known Cathepsin K inhibitory effects were discovered by partial least squares (PLS) method. Evaluation of a test set of 10 compounds with the developed PLS model revealed that this model is reliable with a good predictability. Since the QSAR study was performed on the basis of theoretical descriptors calculated completely from the molecular structures, the proposed model could potentially provide useful information about the activity of the studied compounds. Various tests and criteria such as leave-one-out cross validation, leave-many-out cross validation, and also criteria suggested by Tropsha were employed to examine the predictability and robustness of the developed model.