<i>In Silico</i> Discovery of CXCR4 Inhibitors for Targeted Cancer Therapy via Pharmacophore Modeling and Molecular Simulations
C-X-C motif chemokine receptor 4 (CXCR4) is essential for cellular activities, immunological responses, growth and development. It affects immune cell trafficking, hematopoiesis, organ development and a wide range of pathological processes, including cancer metastasis, HIV infection and inflammatory disorders, making it a prospective therapeutic target for cancer. This work focuses on the discovery of more effective CXCR4 inhibitors. A ZINC library was utilized to screen potential CXCR4 inhibitors using pharmacophore-based modeling. Among the 4218 compounds examined, three compounds: ZINC001023646345, ZINC001266346799 and ZINC001414717906) had substantial binding affinities of −7.8 kcal/mol, −8 kcal/mol and −8.2 kcal/mol with critical CXCR4 residues, indicating their potential as an active inhibitor against CXCR4. Molecular dynamics simulations (MDS) and Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations were also used to assess binding affinity and stability of the protein–ligand complexes. This analysis verified that compound “ZINC001414717906” had higher stability and binding affinity (−27.40 kcal/mol) as compared to the other two compounds, including 1TD, implying that it could be employed as a probable inhibitor to treat cancer and other immune-related diseases. ZINC001414717906 had good dynamic stability, as evidenced by RMSD and RMSF analysis. The results of this investigation demonstrate the efficacy of ZINC001414717906 as a CXCR4 inhibitor with lowest binding free energy, dynamic stability and an adequate ADMET profile. This technique improves our understanding of CXCR4-specific inhibitors and their mechanisms of action, allowing for the creation of tailored therapies.
Authors
- Nahlah Makki Almansour (ORCID: https://orcid.org/0000-0003-3907-7091)
Institutions
- University of Hafr Al-Batin (SA)
Publication Details
- Journal
- Journal of Computational Biophysics and Chemistry
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1142/s2737416525500826
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00