Structure-activity relationship study of flavone compounds with anti-HIV-1 integrase activity: a density functional theory study

Human immunodeficiency virus type-1 integrase (HIV-1 IN) is an essential enzyme for effective viral replication. Flavone compounds have been very much studied due to their activity during the inhibition process of HIV-1 IN. In this study, we employed density functional theory (DFT) using the B3LYP hybrid functional to calculate a set of molecular properties for 32 flavonoid compounds with anti-HIV-1 IN activity. The stepwise discriminant analysis (SDA), principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods were employed to reduce dimensionality and investigate possible relationship between the calculated properties and the anti-HIV-1 IN activity. These analyses showed that the molecular hydrophobicity (ClogP), charge on atom 11 and electrophilic index (omega) are responsible for the separation between anti-HIV-1 IN active and inactive compounds.     

Statistical molecular design of a focused salicylidene acylhydrazide library and multivariate QSAR of inhibition of type III secretion in the Gram-negative bacterium Yersinia

A combined application of statistical molecular design (SMD), quantitative structure–activity relationship (QSAR) modeling and prediction of new active compounds was effectively used to develop salicylidene acylhydrazides as inhibitors of type III secretion (T3S) in the Gram-negative pathogen Yersinia pseudotuberculosis. SMD and subsequent synthesis furnished 50 salicylidene acylhydrazides in high purity. Based on data from biological evaluation in T3S linked assays 18 compounds were classified as active and 25 compounds showed a dose-dependent inhibition. The 25 compounds were used to compute two multivariate QSAR models and two multivariate discriminant analysis models were computed from both active and inactive compounds. Three of the models were used to predict 4416 virtual compounds in consensus and eight new compounds were selected as an external test set. Synthesis and biological evaluation of the test set in Y. pseudotuberculosis and the intracellular pathogen Chlamydia trachomatis validated the models. Y. pseudotuberculosis and C. trachomatis cell-based infection models showed that compounds identified in this study are selective and non-toxic inhibitors of T3S dependent virulence.     

Three-dimensional quantitative structure activity relationships (3-D-QSAR) of antihyperglycemic agents.

A three-dimensional quantitative structure activity relationship study (3-D-QSAR) was performed on a set of thiazolidinedione antihyperglycemic agents using the comparative molecular field analysis (CoMFA) method. The CoMFA models were derived from a training set of 53 compounds. Fifteen compounds, which were not used in model generation were used to validate the CoMFA models. All the compounds were superimposed to the template structure by atom-based and shape-based strategies. The SYBYL QSAR rigid body field fit was also used for aligning the ligands. A total of twelve different alignments were generated. The resulting models exhibited a good cross-validated r2cv values (0.624-0.764) and the conventional r2 values (0.689-0.921). A more robust cross-validation test using cross-validation by 2 groups (leave half out method) was performed 100 times to ascertain the predictiveness of the CoMFA models. The mean of r2cv values from 100 runs ranged from 0.611-0.690. Few models exhibited good external predictivity. These models were then used to define a hypothetical receptor model for antihyperglycemic agents.     

Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.