A quantitative structure-activity relationship study of tetrabutylphosphonium bromide analogs as muscarinic acetylcholine receptors

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

Background:

The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes.

Methods:

In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors.

Results:

The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities.

Conclusion:

Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

In silico modeling for dual inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes in Alzheimer's disease

In this research, we have implemented two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling using two different datasets, namely, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzyme inhibitors. A third dataset has been derived based on their selectivity and used for the development of partial least squares (PLS) based regression models. The developed models were extensively validated using various internal and external validation parameters. The features appearing in the model against AChE enzyme suggest that a small ring size, higher number of -CH2- groups, higher number of secondary aromatic amines and higher number of aromatic ketone groups may contribute to the inhibitory activity. The features obtained from the model against BuChE enzyme suggest that the sum of topological distances between two nitrogen atoms, higher number of fragments X-C(=X)-X, higher number of secondary aromatic amides, fragment R--CR-X may be more favorable for inhibition. The features obtained from selectivity based model suggest that the number of aromatic ethers, unsaturation content relative to the molecular size and molecular shape may be more specific for the inhibition of the AChE enzyme in comparison to the BuChE enzyme. Moreover, we have implemented the molecular docking studies using the most and least active molecules from the datasets in order to identify the binding pattern between ligand and target enzyme. The obtained information is then correlated with the essential structural features associated with the 2D-QSAR models.