QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1

Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors

A structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiled from the ChEMBL database and studied by means of a conformation-independent quantitative structure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptors are explored with the main intention of capturing the most relevant structural characteristics affecting the bioactivity. The structural descriptors are derived with different freeware, such as PaDEL, Mold², and QuBiLs-MAS; such descriptor software complements each other and improves the QSAR results. The best multivariable linear regression models are found with the replacement method variable subset selection technique. The balanced subsets method partitions the dataset into training, validation, and test sets. It is found that the proposed linear QSAR model improves previously reported models by leading to a simpler alternative structure-activity relationship.

Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error

Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.

Therapeutic index modeling and predictive QSAR of novel thiazolidin-4-one analogs against Toxoplasma gondii

The main idea of this study was to find predictive quantitative structure-activity relationships (QSAR) for the therapeutic index of 68 thiazolidin-4-one analogs against Toxoplasma gondii. Multivariate adaptive regression spline (MARS) together with Monte-Carlo (MC) sampling was proposed as a reliable descriptor subset selection strategy. Basis functions and knot points are also determined for each selected descriptor using generalized cross validation after frequency analysis. Least squares-support vector regression (LS-SVR) with optimized hyper-parameters was employed as mapping tool due to its promising empirical performance. The models were validated and tested through the use of the external prediction set of compounds, leave-one-out and leave-many-out cross validation methods, applicability domain analysis and Y-randomization. The robustness and accuracy of the QSAR models were confirmed by the satisfactory statistical parameters for the experimentally reported dataset (R(2)p=0.853, Q(2)LOO=0.785, R(2)L20%O=0.742 and r(2)m=0.715) and low standard error values (RMSEp=0.208, RMSELOO=0.321 and RMSEL20%O=0.376). The comprehensive analysis carried out in the present contribution using the proposed strategy can provide a considerable basis for the design and development of novel drug-like molecules against T.gondii.