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

ObjectiveTo 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. MethodsLigand-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. ResultsThe 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. ConclusionThe 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.