Theoretical approaches to identify the potent scaffold for human sirtuin1 activator: Bayesian modeling and density functional theory

Abstract

Bayesian and pharmacophore modeling approaches were utilized to identify the fragments and critical chemical features of small molecules that enhance sirtuin1 (SIRT1) activity. Initially, 48 Bayesian models (BMs) were developed by exploring 12 different fingerprints (ECFC, ECFP, EPFC, EPFP, FPFC, FPFP, FCFC, FCFP, LCFC, LCFP, LPFC, and LPLP) with diameters of 4, 6, 8, and 10. Among them the BM1 model was selected as the best model based on its good statistical parameters including total accuracy: 0.98 and positive recalls: 0.95. Additionally, BM1 showed good predictive power for the test set (total accuracy: 0.87 and positive recall: 0.87). In addition, 10 qualitative pharmacophore models were generated using 6 well-known SIRT1 activators. Hypothesis2 (Hypo2) was selected as best hypothesis, among 10 Hypos, based on its discriminant ability between the highly active and least/moderately active SIRT1 activators. The best models, BM1 and Hypo2 were used as a query in virtual screens of a drug-like database and the hit molecules were sorted based on Bayesian score and fit value, respectively. In addition, the highest occupied molecular orbital, lowest unoccupied molecular orbital, and energy gap values were calculated for the selected virtual screening hits using density functional theory. Finally, 16 compounds were selected as leads based on their energy gap values, which represent the high reactivity of molecules. Thus, our results indicated that the combination of two-dimensional (2D) and 3D approaches are useful for the discovery and development of specific and potent SIRT1 activators, and will benefit medicinal chemists focused on designing novel lead compounds that activate SIRT1.