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Background: SIRT2 belongs to a class III of Histone Deacetylase (HDAC) and has crucial roles in neurodegeneration and malignancy. Objective: The objective of this study is to discover structurally novel natural-product-derived SIRT2 inhibitors. Methods: Structure-based pharmacophore modeling integrated with validated QSAR analysis was implemented to discover structurally novel SIRT2 inhibitors from the natural products database. The targeted QSAR model combined molecular descriptors with structure-based pharmacophore capable of explaining bioactivity variation of structurally diverse SIRT2 inhibitors. Manually built pharmacophore model, validated with receiver operating characteristic curve, and selected using the statistically optimum QSAR equation, was applied as a 3Dsearch query to mine AnalytiCon Discovery database of natural products. Results : Experimental in vitro testing of highest-ranked hits identified asperphenamate and salvianolic acid B as active SIRT2 inhibitors with IC50 values in low micromolar range. Conclusion: New chemical scaffolds of SIRT2 inhibitors have been identified that could serve as a starting point for lead-structure optimization.

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.

High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an $$\hbox {IC}_{50}$$ value of 237 nM.

Rho Kinase (ROCKII) has been recently implicated in several cardiovascular diseases prompting several attempts to discover and optimize new ROCKII inhibitors. Towards this end we explored the pharmacophoric space of 138 ROCKII inhibitors to identify high quality pharmacophores. The pharmacophoric models were subsequently allowed to compete within quantitative structure-activity relationship (QSAR) context. Genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing self-consistent QSAR of optimal predictive potential (r (77) = 0.84, F = 18.18, r (LOO) (2) = 0.639, r (PRESS) (2) against 19 external test inhibitors = 0.494). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within ROCKII binding pocket. Receiver operating characteristic (ROC) curve analyses established the validity of QSAR-selected pharmacophores. Moreover, the successful pharmacophores models were found to be comparable with crystallographically resolved ROCKII binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds Eight submicromolar ROCKII inhibitors were identified. The most potent gave IC(50) values of 0.7 and 1.0 μM.