<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.

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Journal
Journal of Computational Biophysics and Chemistry
Published
2025-05-30
DOI
https://doi.org/10.1142/s2737416525500826
Primary Topic
Computational Drug Discovery Methods
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article
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article

In Silico Discovery of CXCR4 Inhibitors for Targeted Cancer Therapy via Pharmacophore Modeling and Molecular Simulations

Nahlah Makki Almansour
Journal of Computational Biophysics and Chemistry
Computational Drug Discovery Methods
article

In Silico Discovery of CXCR4 Inhibitors for Targeted Cancer Therapy via Pharmacophore Modeling and Molecular Simulations

Nahlah Makki Almansour
article en

Abstract

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.

Journal of Computational Biophysics and ChemistryVol. 25(07)
University of Hafr Al-Batin (SA)
Good health and well-being
Openalex Percentile: Top 87%
Computational Drug Discovery Methods
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