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Three-dimensional quantitative structure-activity relationship (3D QSAR) methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were applied on a series of 1,4-dihydropyridines possessing antitubercular activity. The study was performed using 33 compounds, in which 22 molecules were used for the derivation of the 3D QSAR models (training set) and 11 molecules were used to evaluate the predictive ability of the derived models (test set). Superimpositions were performed using three alignment rules: atom-based fitting, SYBYL QSAR rigid body field fit of the steric and electrostatic fields of the molecules, and flexible fitting (multifit). Both methods were analyzed in terms of their predictive abilities and produced comparable results with high internal as well as external predictivities. Steric and electrostatic fields of the inhibitors were found to be relevant descriptors for SAR. Use of lowest unoccupied molecular orbital energies or ClogP as additional descriptors in the QSAR table did not improve the significance of the 3D QSAR models. Both CoMFA and CoMSIA models based on multifit alignment showed better correlative and predictive properties than other models. A QSAR study using genetic function approximation was also performed for the same set of molecules using different types of physicochemical descriptors to deal with cell-based activity data. The QSAR models revealed the importance of spatial properties and conformational flexibility of side chains for antitubercular activity. Inclusion of fractional polar solvent accessible surface area as a descriptor in the model generation resulted in models with significant internal and external predictivities for the same test set molecules, which may support the possible mode of action of these compounds.

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The histone deacetylase enzyme has increasingly become an attractive target for developing novel anticancer drugs. Hydroxamates are a new class of anticancer agents reported to act by selective inhibition of the histone deacetylase (HDAC) enzyme. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to study three-dimensional quantitative structure-activity relationships (3D-QSARs). QSAR models were derived from a training set of 40 molecules. An external test set consisting of 17 molecules was used to validate the CoMFA and CoMSIA models. All molecules were superimposed on the template structure by atom-based, multifit and the SYBYL QSAR rigid body field fit alignments. The statistical quality of the QSAR models was assessed using the parameters r(2)(conv), r(2)(cv) and r(2)(pred). In addition to steric and electronic fields, ClogP was also taken as descriptor to account for lipophilicity. The resulting models exhibited a good conventional r(2)(conv) and cross-validated r(2)(cv) values up to 0.910 and 0.502 for CoMFA and 0.987 and 0.534 for CoMSIA. Robust cross-validation by 2 groups was performed 25 times to eliminate chance correlation. The CoMFA models exhibited good external predictivity as compared to that of CoMSIA models. These 3D-QSAR models are very useful for design of novel HDAC inhibitors.

A three-dimensional quantitative structure-activity relationship (QSAR) study using the comparative molecular field analysis (CoMFA) method was performed on a series of interleukin 1-beta converting enzyme (ICE) inhibitors. The compounds studied have been reported to be time-dependent inhibitors of ICE. This study was performed using 49 compounds, in which the CoMFA models were developed using a training set of 39 compounds. All the compounds were modeled using the X-ray crystal structure of tetrapeptide aldehyde inhibitor/ICE complex. The inhibitor compounds were considered both as neutral species and as P1 carboxylate ionized species. Superimpositions were performed using two alignment rules, namely, an alignment of the structures based on RMS fitting of the backbone heavy atoms of each structure to compound 2 and an alignment based on SYBYL QSAR rigid body field fit of the steric and electrostatic fields of the molecules to the fields of compound 2. Use of LUMO energies or ClogP as additional descriptors in the QSAR table did not improve the significance of the CoMFA models. Steric and electrostatic fields of the inhibitors were found to be the relevant descriptors for structure-activity relationships. The predictive ability of the CoMFA model was evaluated by using a test set of 10 compounds (r2pred as high as 0.859). Further comparison of the coefficient contour maps with the steric and electrostatic properties of the receptor show a high level of compatibility.

Cyclooxygenase-2 (COX-2) inhibitors are widely used for the treatment of pain and inflammatory disorders such as rheumatoid arthritis and osteoarthritis. A series of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives has been reported as COX-2 inhibitors. In order to understand the structural requirement of these COX-2 inhibitors, a ligand-based pharmacophore and atom-based 3D-QSAR model have been developed. A five-point pharmacophore with four hydrogen bond acceptors (A) and one hydrogen bond donor (D) was obtained. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square (PLS) statistics results. The training set correlation is characterized by PLS factors (r (2) = 0.642, SD = 0.65, F = 82.7, P = 7.617 e - 12). The test set correlation is characterized by PLS factors (Q (2) (ext) = 0.841, RMSE = 0.24,Pearson-R = 0.91). A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Arg513, Val523, Phe518, Ser530, Tyr355, His90) of COX-2 enzyme. The results of ligand-based pharmacophore hypothesis and atom-based 3D-QSAR give detailed structural insights as well as highlights important binding features of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives as COX-2 inhibitors which can provide guidance for the rational design of novel potent COX-2 inhibitors.

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