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2000
Volume 21, Issue 3
  • ISSN: 1573-4099
  • E-ISSN: 1875-6697

Abstract

Background

Histone deacetylase 9 (HDAC9) is an important member of the class IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer, lung cancer, lymphoblastic leukaemia, . The important role of HDAC9 is also recognized in the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to find out the important structural attributes of HDAC9 inhibitors for developing selective HDAC9 inhibitors with higher potency.

Methods

The classification QSAR-based methods namely Bayesian classification and recursive partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural features strongly suggested that sulphur-containing compounds can be a good choice for HDAC9 inhibition. For this reason, these models were applied further to screen some natural compounds from . The screened compounds were further accessed for the ADME properties and docked in the homology-modelled structure of HDAC9 in order to find important amino acids for the interaction. The best-docked compound was considered for molecular dynamics (MD) simulation study.

Results

The classification models have identified good and bad fingerprints for HDAC9 inhibition. The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide had been identified as compounds having potent HDAC9 inhibitory activity. The results from ADME and molecular docking study of these compounds show the binding interaction inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory results in terms of different validation parameters of MD simulation study.

Conclusion

This modelling study has identified the natural potential lead (s) from . Specifically, the ajoene with the best features can be considered for further and investigation to establish as potential HDAC9 inhibitors.

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2025-09-28

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Exploration of Fingerprints and Data Mining-based Prediction of Some Bioactive Compounds from Allium sativum as Histone Deacetylase 9 (HDAC9) Inhibitors
Curr. Computeraided Drug Des. 21, 270 (2025); https://doi.org/10.2174/0115734099282303240126061624
/content/journals/cad/10.2174/0115734099282303240126061624
/content/journals/cad/10.2174/0115734099282303240126061624
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  • Article Type:     Research Article
Keyword(s): Allium sativum; bayesian classification; Histone deacetylase 9 (HDAC9); molecular docking; molecular dynamics simulation; recursive partitioning tree

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