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Exploring the anticancer potential of some azaflavanones derivatives through molecular docking studies

  • Djawhara Haddad ORCID logo EMAIL logo , Nasser Belboukhari ORCID logo , Khaled Sekkoum ORCID logo and Meriem Bouanini ORCID logo
Published/Copyright: June 18, 2025
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Abstract

Azaflavanones are natural or synthetic compounds that have shown promising biological activities, including anticancer properties. Pim-1 is a serine/threonine kinase that plays a critical role in various cellular processes, and its overexpression has been associated with several types of cancer, making it an attractive target for anticancer drug development. This study aims to investigate the molecular docking properties of 10 azaflavanone derivative enantiomers against Pim-1 enzymes as a potential anticancer strategy. The docking simulations were carried out with the MOE software’s default settings, with the binding site set to Pim-1’s active site. Binding affinity analysis was performed based on RMSD values, binding energy, and intermolecular interactions such as hydrophobic interaction, π-interaction, and van der Waals for the enantiomers of all compounds. Among the 10 chemical compounds, there are three compounds: 2-(2,5-Dimethylphenyl)-2,3-dihydroquinoline-4(1H)-one, 2-(4-methoxyphenyl)-2,3-dihydroquinoline-4(1H)-one and 2-mesetyl-2,3-dihydroquinoline-4(1H) -one gave good results, with binding energies ranging from −5.7258 to −6.3164 kcal/mol, with RMSD values ranging from 1.0735 to 1.0735 Å. The molecular docking studies demonstrate that azaflavanones derivatives have the potential to serve as promising anticancer inhibitors targeting Pim-1 enzymes.


Corresponding author: Djawhara Haddad, Bioactive Molecules and Chiral Separation Laboratory, Faculty of Exact Sciences, Tahri Mohammed University, Istiklal Street PO 417, Bechar, 08000, Algeria, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: Conceptualization: Nasser Belboukhari, Djawhara Haddad; methodology: Djawhara Haddad; validation: Nasser Belboukhari, Khaled Sekkoum; formal analysis: Meriem Bouanini.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: All data generated or analyzed during this study are included in this published article.

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Received: 2025-02-27
Accepted: 2025-06-05
Published Online: 2025-06-18

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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