Design and combinatorial library generation of azole derivatives as anticancer agents: QSAR, molecular docking and dynamic approach
Azole scaffolds have gained significant attention in anticancer drug discovery due to their diverse pharmacological potential and favourable physicochemical properties. A ligand-based quantitative structure-activity relationship (QSAR) study was conducted on 63 azole derivatives with reported cytotoxic activity against liver (HA22T) and gastric (NUGC) cancer cell lines using QSARINS. Statistically robust multiple linear regression (MLR) models were generated (R² > 0.70, Q² > 0.60) and validated through internal and external parameters along with the leverage-based applicability domain. Guided by the derived structural insights, 150 new azole derivatives were designed using SmiLib software and evaluated for ADMET and drug-likeness properties via SwissADME. Molecular docking was performed against EGFR (PDB ID: 6V6O) and HER2 (PDB ID: 3PP0) using Discovery Studio, followed by 100 ns molecular dynamics (MD) simulations in GROMACS to confirm complex stability. All designed molecules satisfied drug-likeness filters and exhibited favourable pharmacokinetic profiles. Docking studies identified compounds 8c (HER2) and 12 l (EGFR) with strong binding affinities (-CDOCKER interaction energy −47.1 and −47.0 kcal/mol, respectively) and stable hydrogen bond networks with key active site residues. MD simulations demonstrated minimal RMSD fluctuations (<0.4 Å), validating the conformational stability of the ligand–protein complexes. An integrated QSAR, docking, and MD approach successfully identified novel azole derivatives with promising anticancer potential. Compounds 8c and 12 l are proposed as lead scaffolds for further synthesis and biological evaluation.
Authors
- Balaji Wamanrao Matore (ORCID: https://orcid.org/0000-0002-6147-5515)
- Purusottam Banjare (ORCID: https://orcid.org/0000-0002-8085-588X)
- Anjali Murmu (ORCID: https://orcid.org/0000-0001-8783-4275)
- Partha Pratim Roy
- Jagadish Singh
Institutions
- Guru Ghasidas Vishwavidyalaya (IN)
- Centurion University of Technology and Management (IN)
Publication Details
- Journal
- Letters in Drug Design & Discovery
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.lddd.2025.100267
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00