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Adenosine A2A receptor antagonists are of great interest in the treatment for Parkinson’s disease. In this study, we combined extensive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent Adenosine A2A antagonists. Genetic function algorithm (GFA) joined with k nearest neighbor (kNN) analyses were applied to build predictive QSAR models. Successful pharmacophores were complemented with exclusion spheres to improve their receiver operating characteristic curve (ROC) profiles. Best QSAR models and their associated pharmacophore hypotheses were validated by identification of several novel Adenosine A2A antagonist leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated IC50 value of 545.7 nM.

Mammalian target of rapamycin (mTOR) is a serine/threonine kinase and member of the PI3K-related kinase (PIKK) family. It plays a central role in integrating signals from metabolism, energy homeostasis, cell cycle, and stress response. Aberrant PI3K/mTOR activation is commonly observed in diseases such as cancer, diabetes and Alzheimer's disease. Accordingly, we developed common feature binding hypotheses for a set of 6 potent mTOR antagonists. The generated models were validated using receiver operating characteristic (ROC) curve analyses. To gain better insight into ligand–mTOR interactions, a homology model for the kinase domain of mTOR was built using the crystallographic structure of PI3Kγ as template. The optimal pharmacophore model was further improved based on detailed docking studies of potent training compound in the homology model. The modified binding model was employed as 3D search query to screen our in-house-built database of established drugs. Subsequent in vitro screening of captured hits showed that six of them have submicromolar to low micromolar bioactivities, namely, glyburide, metipranolol, sulfamethizole, glipizide, pioglitazone, and sotalol.