Medicinal Chemistry Research

, Volume 23, Issue 9, pp 3998–4010 | Cite as

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

  • Sugunadevi Sakkiah
  • Mahreen Arooj
  • Keun Woo Lee
  • Jorge Z. TorresEmail author
Original Research


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.


Sirtuin Bayesian model Density functional theory Ligand-based pharmacophore model Activator 



Absorption, distribution, metabolism, excretion and toxicity


Becke exchange plus Lee–Yang–Parr correlation


Bayesian model


Blood–brain barrier


Chemistry at HARvard Macromolecular Mechanics


Cytochrome P450


Density functional theory


Discovery Studio v3.1


Atom type extended connectivity fingerprints counts


Atom type connectivity fingerprints counts


Atom type extended connectivity fingerprints


Functional class daylight path-based fingerprints


Functional class extended connectivity fingerprints counts


Functional class extended connectivity fingerprints


False negative


False positive


Functional class daylight path-based fingerprint counts


Functional class daylight path-based fingerprints


Hydrophobic aliphatic


Hydrophobic aromatic


Hydrogen-bond acceptor


Hydrogen-bond donor


Histone deacetylase


Human intestinal adsorption


Highest occupied molecular orbital






ALogP types extended connectivity fingerprint counts


ALogP extended connectivity fingerprint


ALogP types daylight path-based fingerprint counts


ALogP types daylight path-based fingerprints


Lowest unoccupied molecular orbital


Mathew’s correlation coefficient


Molecular mechanics


Negative ionizable


Positively ionized or positively ionizable


Plasma protein binding


Quantum mechanics


Quantitative structure–activity relationships


Ring aromatic


Receiver operating characteristic




True negatives


True positives



This work was supported by the Basic Science Research Program (2012R1A1A4A01013657), Pioneer Research Center Program (2009-0081539), the Management of Climate Change Program (2010-0029084) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, and the Next Generation BioGreen 21 Program (PJ009486) from Rural Development Administration (RDA) of Republic of Korea.

Conflict of interest

The authors declare no competing financial interests.

Supplementary material

44_2014_983_MOESM1_ESM.docx (86 kb)
Supplementary material 1 (DOCX 85 kb)


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Sugunadevi Sakkiah
    • 1
  • Mahreen Arooj
    • 2
  • Keun Woo Lee
    • 2
  • Jorge Z. Torres
    • 1
    • 3
    • 4
    Email author
  1. 1.Department of Chemistry and BiochemistryUniversity of CaliforniaLos AngelesUSA
  2. 2.Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS)Gyeongsang National University (GNU)JinjuSouth Korea
  3. 3.Jonsson Comprehensive Cancer CenterUniversity of CaliforniaLos AngelesUSA
  4. 4.Molecular Biology InstituteUniversity of CaliforniaLos AngelesUSA

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