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The c-Jun N-terminal kinases are attractive targets because of their involvement in several diseases. In this work, a combined molecular modeling study for a set of isoquinolones as inhibitors of JNK1 was performed by molecular docking, genetic algorithm-multiple linear regression and comparative molecular field analysis to rationalize the structural requirements responsible for the inhibitory activity of these compounds. Molecular docking study was employed to explore the binding mode of the active compound at the active site of JNK1. Based on the docked conformations, highly predictive 2D, 3D quantitative structure-activity relationship models were developed. The best 2D quantitative structure-activity relationship model was established using genetic algorithm-multiple linear regression method containing four molecular descriptors. The best comparative molecular field analysis model was obtained with a cross-validated coefficient q of 0.562, non-cross-validated r values of 0.994. The information from quantitative structure-activity relationship models and molecular docking is useful for the design of novel JNK1 inhibitors with improved activities. Structural Requirements of Isoquinolones as Novel Selective c-Jun N-terminal Kinase 1 Inhibitors: 2D and 3D QSAR Analyses Juan Du, Lili Xi, Beilei Lei, Huanxiang Liu and Xiaojun Yao* A combined molecular modeling study for a set of isoquinolones as inhibitors of c-Jun N-terminal kinase 1 (JNK1) was performed by molecular docking, genetic algorithm-multiple linear regression and comparative molecular field analysis to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The result will be useful for the design of novel selective JNK1 inhibitors with potential inhibitory activity. [ABSTRACT FROM AUTHOR]

Structure-based quantitative structure-activity relationship (QSAR) studies on a series of checkpoint kinase 1 (Chk1) inhibitors were performed to find the key structural features responsible for their inhibitory activity. Molecular docking was employed to explore the binding mode of all inhibitors at the active site of Chk1 and determine the active conformation for the QSAR studies. Ligand and structure-based descriptors incorporating the ligand-receptor interaction were generated based on the docked complex. Genetic Algorithm-Multiple Linear Regression (GA-MLR) method was used to build 2D QSAR model. The 2D QSAR model gave a squared correlation coefficient R2 of 0.887, cross-validated Q2 of 0.837 and the prediction squared correlation coefficient R2pred of 0.849, respectively. Furthermore, three-dimensional quantitative structure-activity relationship (3D QSAR) model using comparative molecular field analysis (CoMFA) with R2 of 0.983, Q2 of 0.550 and R2pred of 0.720 was also developed. The obtained results are helpful for the design of novel Chk1 inhibitors with improved activities. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]

In the quantitative structure-activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave-one-out cross-validation (LOO-CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO-CV error or highest LOO-CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross-validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin-concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO-CV LLR methods and the traditional global linear model. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]

In this study, a novel method was developed to predict the binding affinity of protein-ligand based on a comprehensive set of structurally diverse protein-ligand complexes (PLCs). The 1300 PLCs with binding affinity (493 complexes with Kd and 807 complexes with Ki) from the refined dataset of PDBbind Database (release 2007) were studied in the predictive model development. In this method, each complex was described using calculated descriptors from three blocks: protein sequence, ligand structure, and binding pocket. Thereafter, the PLCs data were rationally split into representative training and test sets by full consideration of the validation of the models. The molecular descriptors relevant to the binding affinity were selected using the ReliefF method combined with least squares support vector machines (LS-SVMs) modeling method based on the training data set. Two final optimized LS-SVMs models were developed using the selected descriptors to predict the binding affinities of Kd and Ki. The correlation coefficients (R) of training set and test set for Kd model were 0.890 and 0.833. The corresponding correlation coefficients for the Ki model were 0.922 and 0.742, respectively. The prediction method proposed in this work can give better generalization ability than other recently published methods and can be used as an alternative fast filter in the virtual screening of large chemical database. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009 [ABSTRACT FROM AUTHOR]

Quantitative structure-activity relationship (QSAR) of a series of structural diverse malonyl-CoA decarboxylase (MCD) inhibitors have been investigated by using the predictive single model as well as the consensus analysis based on a new strategy proposed by us. Self-organizing map (SOM) neural network was employed to divide the whole data set into representative training set and test set. Then a multiple linear regressions (MLR) model population was built based on the theoretical molecular descriptors selected by Genetic Algorithm using the training set. In order to analyze the diversity of these models, multidimensional scaling (MDS) was employed to explore the model space based on the Hamming distance matrix calculated from each two models. In this space, Q2 (cross-validated R2) guided model selection (QGMS) strategy was performed to select submodels. Then consensus modeling was built by two strategies, average consensus model (ACM) and weighted consensus model (WCM), where each submodel had a different weight according to the contribution of model expressed by MLR regression coefficients. The obtained results prove that QGMS is a reliable and practical method to guide the submodel selection in consensus modeling building and our weighted consensus model (WCM) strategy is superior to the simple ACM. © 2008 Wiley Periodicals, Inc. J Comput Chem 2008 [ABSTRACT FROM AUTHOR]