Comprehensive in silico discovery of c-Src tyrosine kinase inhibitors in cancer treatment: A unified approach combining pharmacophore modeling, 3D QSAR, DFT, and molecular dynamics simulation

Abstract

Objective: To investigate c-Src, a non-receptor tyrosine kinase dysregulated in various cancer types including colon, breast, and pancreatic cancers, as a potential drug target for cancer therapy. Methods: Ligand-based pharmacophore modeling and 3D-QSAR analysis on a dataset of 34c-Src tyrosine kinase inhibitors were employed. The established pharmacophore model (DDRRR_1) features two hydrogen bond donor (D) and three aromatic ring (R) features, exhibiting favorable parameters (R2 = 0.926; Q2 = 0.895; F value = 47.9). Hypothesis validation, enrichment analysis, and contour plot analysis were conducted, followed by virtual screening of a PubChem database using the optimized pharmacophore model and filtering based on the Lipinski rule of five. Results: The most promising inhibitors underwent multistep molecular docking, density Functional Theory (DFT) analysis, ADMET assessments, molecular dynamics simulation, and PCA. CID_70144047 emerged as the most promising hit with all the above favorable properties. Conclusion: The study provides a comprehensive approach for identifying novel c-Src tyrosine kinase inhibitors, highlighting CID_70144047 as a promising leads with potential therapeutic applications in cancer treatment.

Keywords

• c-Src tyrosine kinase
• Pharmacophore modeling
• Virtual screening
• Drug discovery
• DFT analysis
• Molecular dynamics simulation