In this study we have investigated the relative correlation potential of Wiener (W), Szeged (Sz), and molecular connectivity indices (0chiR, 1chiR and 2chiR) in developing quantitative structure-activity relationships, QSAR; log P values of benzoic acid and its nuclear-substituted derivatives were used for this purpose. The statistical analyses for univariate and multivariate correlations had indicated that both W and Sz are closely related to the connectivity indices (mchiR) and that the W, the Sz, and the 1chiR indices have similar modeling potentials. 1chiR gives slightly better results than both W and Sz. Other connectivity indices 0chiR and 2chiR correlate poorly with log P.

At present, chemotherapy seems to be the main weapon in the arsenal of remedies for the ongoing crusade against AIDS. The mode of binding of the TIBO family of inhibitors has been of interest because these compounds do not fit the two-hinged-ring model as generally observed in the NNRTIs. Flexible docking simulations were performed with a series of 53 TIBO derivatives as NNRTIs. Binding preferences as well as the structural and energetic factors associated with them were studied. A good correlation (r(2)=0.849, q(2)=0.843) was observed between the biological activity and binding affinity of the compounds which suggest that the identified binding conformations of these inhibitors are reliable. Further screening of PubChem database yielded novel scaffolds. Our studies suggest that modifications to the TIBO group of inhibitors might enhance their binding efficacy and hence, potentially, their therapeutic utility.

Lipophilicity or hydrophobicity is a crucial physico-chemical property of an oral drug compound. In the present study, we have analysed the structural parameters responsible for enhancing the lipophilicity expressed in terms of Octanol-Water partition coefficient, log P, of 2-amino-6-arylsulfonylbenzonitrile (AASBN) derivatives used as NNRTIs in AIDS therapy. Connectivity based Randic (χ) and Balaban (J) and atomistic Kier-Hall electrotopological state (E-state) indices have been used to develop Quantitative Structure-Property Relationship (QSPR) and to predict the effect of substitution on the log P. Model has been developed using multiple linear regression analysis (MLR) for the training set (67 compounds) and the model was tested on a test set (7 compounds). Significant results were obtained for the training set (R 2 = 0⋅948, R 2 adj = 0⋅939, SE = 0⋅177, F-ratio = 101⋅22). The results of the test set too implicated a good fit (R 2 = 0⋅941, R 2 adj = 0⋅929, SE = 0⋅157, F-ratio = 80⋅05). Among the two connectivity based topological indices; Randic (χ) index showed better predictive ability than the Balaban (J) index. Kier-Hall E-state indices indicated that among the functional groups, methyl, bromo, chloro groups on ring A, with their positive coefficients enhanced the lipophilicity. Amino, cyano group on ring B and the bridging S, SO, SO 2 with their negative coefficients showed an adverse effect on the lipophilicity parameter. Thus, Kier-Hall E-state indices along with topological indices could be well applied for deriving QSPR models and analysing substitution effects of various functional groups. The training set, correlation matrix and observed and experimental log P values are available as supplementary material for this article.

Current challenges in drug designing and lead optimization has reached a bottle neck where the main onus lies on rigorous validation to afford robust and predictive models. In the present study, we have suggested that predictive structure-activity relationship (SAR) models based on robust statistical analyses can serve as effective screening tools for large volume of compounds present either in chemical databases or in virtual libraries. 3D descriptors derived from the similarity-based alignment of molecules with respect to group center overlap from each individual template point and other "alignment averaged," but significant descriptors (ClogP, molar refractivity, connolly accessible area) were used to generate QSAR models. The results indicated that the artificial neural network method (r(2) = 0.902) proved to be superior to the multiple linear regression method (r(2) = 0.810). Cross validation of the models with an external set was reasonably satisfactory. Screening PubChem compound database based on the models obtained, yielded 14 newer modified compounds belonging to the TIBO class of inhibitors, as well as, two novel scaffolds, with enhanced binding efficacy as hits. These hits may be targeted toward potent lead-optimization and help in designing and synthesizing new compounds with potential therapeutic utility.

Quantitative structure-activity relationships (QSAR), based on E-state indices have been developed for a series of tetrahydroimidazo-[4,5,1-jk]-benzodiazepinone derivatives against HIV-1 reverse transcriptase (HIV-1 RT). Statistical modeling using multiple linear regression technique in predicting the anti-HIV activity yielded a good correlation for the training set (R(2) = 0.913, R(2)(adj) = 0.897, Q(2) = 0.849, MSE = 0.190, F-ratio = 59.97, PRESS = 18.05, SSE = 0.926, and p value = 0.00). Leave-one-out cross-validation also reaffirmed the predictions (R(2) = 0.850, R(2)(adj) = 0.824, Q(2) = 0.849, MSE = 0.328, and PRESS = 18.05). The predictive ability of the training set was also cross-validated by a test set (R(2) = 0.812, R(2)(adj) = 0.799, Q(2) = 0.765, MSE = 0.347, F-ratio = 64.69, PRESS = 7.37, SSE = 0.975, and p value = 0.00), which ascertained a satisfactory quality of fit. The results reflect the substitution pattern and suggest that the presence of a bulky and electropositive group in the five-member ring and electron withdrawing groups in the seven-member ring will have a positive impact on the antiviral activity of the derivatives. Bulky groups in the six-member ring do not show an activity-enhancing impact. Outlier analysis too reconfirms our findings. The E-state descriptors indicate their importance in quantifying the electronic characteristics of a molecule and thus can be used in chemical interpretation of electronic and steric factors affecting the biological activity of compounds.

Researchers are on the constant lookout for new antiviral agents for the treatment of AIDS. In the present work, ligand based modeling studies are performed on analogues of substituted phenyl-thio-thymines, which act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) and novel leads are extracted. Using alignment-dependent descriptors, based on group center overlap (SALL, HDALL, HAALL and RALL), an alignment-independent descriptor (S log P), a topological descriptor (Balaban index (J)) and a 3D descriptor dipole moment (μ) and shape based descriptors (Kappa 2 index ((2)κ)), a correlation is derived with inhibitory activity. Linear and non-linear techniques have been used to achieve the goal. Support Vector Machine (SVM, R = 0.929, R(2) = 0.863) and Back Propagation Neural Network (BPNN, R = 0.928, R(2) = 0.861) methods yielded near similar results and outperformed Multiple Linear Regression (MLR, R = 0.915, R(2) = 0.837). The predictive ability of the models are cross-validated using a test dataset (SVM: R = 0.846, R(2) = 0.716, BPNN: R = 0.841, R(2) = 0.707 and MLR: R = 0.833, R(2) = 0.694). It is concluded that the hydrophobicity (S log P) and the polarity (μ) of a ligand and the presence of hydrogen donor (HDALL) moieties are the deciding factors in improving antiviral activity and pharmaco-therapeutic properties. Based on the above findings, a virtual dataset is created to extract probable leads with reasonable antiviral activity as well as better pharmacophoric properties.

ABSTRACT

A template-based flexible docking simulation followed by 'Tabu-clustering' was performed on a series of 38 TIBO derivatives as HIV-1 reverse transcriptase (HIV-1 RT) inhibitors. Four different templates of the Cl-TIBO (1-REV) were created and used as reference templates for docking and aligning. On the basis of the optimal conformation of the ligands, when fitting to the template, the respective scoring functions were obtained; different ligand efficiencies were evaluated and analysed. Statistical modelling using artificial neural network (ANN: r 2 = 0⋅922) and multiple linear regression method (MLR: r 2 = 0⋅851) showed good correlation between the biological activity, binding affinity, and different ligand efficiencies of the compounds, which suggest the robustness of the template-based binding conformations of these inhibitors. Our studies suggest that, template-based docking followed by 'Tabuclustering' will give a better alignment of inhibitors with respect to the crystal coordinates and enhance the docking efficiency. Also, our study indicates that scoring functions based on 3D symmetry analysis along with heavy atoms count serve as a valuable tool for estimating the efficiency of the ligands. Thus, this is a novel method based on heavy atoms count predicting the binding affinity of the TIBO group of inhibitors, so that their therapeutic utility can be enhanced.Keywords. TIBO; structure based drug design (SBDD); template; Tabu-clustering; HIV-1 reverse transcriptase (HIVRT); non-nucleoside reverse transcriptase inhibitor (NNRTI); pIC50; multiple linear regression (MLR); artificial neural network (ANN).