Atom-based 3D-QSAR, pharmacophore modelling and molecular dynamics guided identification of thienopyrimidinone derivatives targeting EGFR in breast cancer.
Breast cancer still ranks to be one of the primary causes of cancer-related death worldwide, underscoring the critical need for innovative targeted treatment agents with enhanced safety and efficacy. The current work examined Thienopyrimidinone-based derivatives as possible EGFR inhibitors for breast cancer treatment using an integrated computational drug discovery approach. An atom-based 3D-QSAR model was developed and statistically validated, exhibiting satisfactory predictive performance (R2 = 0.88 and Q2 = 0.55) and showed strong predictive numbers when checked contour maps. Hydrophobic and electron-withdrawing substitutions around the core scaffold were predicted to contribute significantly to biological activity. Pharmacophore modelling identified key structural features associated with EGFR inhibitory activity, including hydrophobic interactions, aromatic ring contacts, and hydrogen bond donor/acceptor sites, which facilitated ligand recognition. ADMET prediction and Lipinski's Rule of Five evaluation indicated that several designed derivatives possessed favourable predicted pharmacokinetic properties, favourable predicted pharmacokinetic properties and oral bioavailability, and satisfactory drug-likeness. Molecular docking studies against the mutant EGFR kinase domain (PDB ID: 6LUD) revealed favourable binding of the selected lead compounds within the ATP-binding site, supported by hydrogen-bonding and hydrophobic interactions. These investigations also revealed considerable hydrogen bonding, hydrophobic interactions, and target bound conformational orientation similar to that of reference compounds. A 100-ns molecular dynamics simulation of the EGFR-Mol 13 complex further demonstrated overall complex stability, with persistent protein-ligand interactions and adaptive ligand conformational behaviour. Overall, the integrated QSAR, pharmacophore modelling, molecular docking, ADMET prediction, and molecular dynamics analyses suggest that thienopyrimidinone derivatives represent promising computational lead compounds for the future design and experimental evaluation of EGFR-targeted therapies for breast cancer.