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In this work the aqueous solubilities of 145 drug-like compounds were predicted from their theoretical derived molecular descriptors. Descriptors which were selected by stepwise multiple subset selection methods are; 1st-order solvation connectivity index, average span R, overall hydrogen bond basicity, and percent of hydrophilic surface area. These descriptors can encode features of molecules which are effected on dispersion, hydrophobic and steric interactions between solute and solvent molecules. To develop quantitative structure–activity relationship (QSAR) models, the methods of multiple linear regressions, least-squares support vector machine, and artificial neural network (ANN) were used by applying the selected descriptors as their inputs. The obtained statistical parameters of these models revealed that ANN model was superior to other methods. The standard error (SE), average error (AE), and average absolute error (AAE) for ANN model are: SE = 0.714, AE = −0.178, and AAE = 0.546, while these values for internal test set are: SE = 0.830, AE = −0.056, and AAE = 0.630 and for external test set are: SE = 0.762, AE = −0.431, and AAE = 0.626, respectively. Moreover the leave-many-out cross validation test was used to further investigate the prediction power and robustness of model, which lead to RL10O2 = 0.816 and SPRESS = 0.32 for ANN model, which revealed the reliability of this model.

Comparative molecular field analysis (CoMFA), topomer CoMFA, and hologram QSAR as three efficient methods of QSAR have been performed on 40 newly synthesized inhibitors against HIV-1 protease. Molecular alignment was performed by aid of crystallographic structure of template inhibitor (indirect alignment) and also by the molecular mechanic (MM)-minimized structure. Both alignment methods produced satisfactory statistics for training set, but indirect alignment had more predictive power. Generated counter maps, especially by topomer CoMFA, give comprehensive information about structural features affecting the inhibitory activities of studied chemicals. Based on the obtained information, some new inhibitors were suggested.

In this study, a new hybrid docking-quantitative structure–property relationship (QSPR) methodology was used to model and predict the adsorption coefficients of some small organic compounds on pristine multiwall carbon nanotube (MWCNT). In this method, descriptors are calculated from the reproduced experimental conformations by molecular docking to develop predictive QSPR models. Three MLR models with squared correlation coefficient ([Formula: see text] values of 0.93, 0.94 and 0.95 were selected. The prediction power of models was evaluated on 12-member test set, which was not used during the modeling and led to [Formula: see text] values of 0.88, 0.85 and 0.93. This methodology gives new insight into factors influenced on the adsorption of nanoparticles.