“Computational identification of stilbene-based EGFR inhibitors using QSAR modeling, molecular docking, and dynamics simulations”
Om G. Nagras, Anushka V. Shinde, Tanaya P. Kawade, Shantanu A. Joshi, Somdatta Chaudhari, Rushikesh Shinde · In Silico Research in Biomedicine · 2026
One of the leading causes of death for women is still breast cancer, especially its deadly triple-negative subtype. Stilbene derivatives were investigated as new anticancer drugs that target the EGFR tyrosine kinase domain because of their wide range of pharmacological potential. This study used Chem Draw and Schrödinger software to perform a 3D QSAR analysis of 32 stilbene derivatives. Glide was then used for molecular docking investigations with the EGFR kinase domain (PDB ID: 1M17). QikProp was utilized to evaluate ADMET characteristics, while Desmond was used to run molecular dynamics simulations and estimate binding free energies using MM-GBSA. With an R2 value of 0.7725, the top 3D QSAR model demonstrated excellent predictive power. Several developed compounds had better binding affinities than erlotinib, according to docking results. Positive pharmacokinetic and stability profiles were validated by ADMET and MD tests, indicating stilbene derivatives as possible candidates for EGFR-targeted breast cancer treatment.