The cooperative effects between T-shape stacking and hydrogen bond interactions in X-ben\pyrÁÁÁH-F complexes were investigated in this work. The results indicate that the electron-withdrawing/donating substituents decrease/increase the magnitude of the binding energies compared to the unsubstituted X-ben\pyrÁÁÁH-F (X = H) complex. The cooperative effects have been studied while using the atoms in molecules (AIM) and natural bond orbital (NBO) methods, allowing us to evaluate the interplay between T-shape stacking and hydrogen bond interactions. There are good relationships among binding energies, Hammett constants, geometrical parameters, and the results of AIM and NBO analysis in X-ben\pyrÁÁÁH-F complexes.

Accurate and robust classification models for describing and predicting the activity of 330 chemicals that are sphingosine kinase 1 (SphK1) and/or sphingosine kinase 2 (SphK2) inhibitors were derived. The classification models developed in this work assist in finding selective subspaces in chemical space occupied by particular groups of SphK inhibitors. A combination of a genetic algorithm (GA) and a counter propagation artificial neural network (CPANN) was utilized to select the most efficient subsets of the molecular descriptors. The optimized models in this work reasonably separate active inhibitors of SphK1 from active SphK2 inhibitors. Generally, the CPANN models in this work were used to classify the compounds according to their therapeutic targets and activities. The simplicity of the chosen descriptors and their relative importance sheds some light on the structural features necessary to induce selective inhibitory activity to the studied molecules. The areas under the receiver operating characteristic (ROC) curves for the GA-CPANN models in this work were 0.934 and 0.922 for active SphK1 and SphK2 inhibitors, respectively. Generally, the results in this work suggest some important molecular features and pharmacophores that could help medicinal chemists develop selective and potent SphK inhibitors.

Selective inhibition of Bcl-2 and Bcl-x proteins due to their dual inhibition toxicity plays an important role in treatment of cancer and chemotherapy effectiveness; therefore, in the last decade, discovery of selective inhibitors for Bcl-2 and Bcl-x proteins has become a significant and important research topic. The present contribution paves the way for characterization of molecular features which induce selectivity for inhibition of Bcl-2 and Bcl-x. In this line, a total of 1534 molecules related to inhibition of Bcl-2 and Bcl-x proteins were collected from Binding Database. A diverse set of molecular descriptors was calculated for each molecule, and the best subset of descriptors were selected using variable importance in projection (VIP) approach. The molecules were classified according to their therapeutic targets (Bcl-2/Bcl-x) and activities. Partial least square-discriminate analysis (PLS-DA) and supervised Kohonen network (SKN) models were utilized to relate the molecular structures of chemicals to their activities and selectivities. According to the VIP-selected descriptors physicochemical properties, such as polarity number, number of branches, size and cyclicity of the molecule, flexibility, functional counts and constitutional descriptors, all affect the activities of Bcl-2 and Bcl-x inhibitors. The performances of PLS-DA and SKN methods were evaluated based on statistical parameters derived from the confusion matrices. The models were validated using tenfold cross-validation and an external test set. The best statistical results were obtained by implementing the SKN model. The classification rates range from 93.5 to 79.1% for the training and validation procedure for the optimized SKN models. The high values of the obtained classification rates demonstrate that the information provided in this work would be useful to design new drugs with selective inhibitory activities toward Bcl-2 or Bcl-x proteins for more effective treatment of cancer.

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