Computational profiling of flavonoids against key breast cancer targets: an in-silico exploration

Breast cancer remains a major global health concern, underscoring the need for new, multitarget therapeutic strategies. This study employed an integrative computational approach combining molecular docking, MM/GBSA binding free energy analysis, ADMET profiling, and Density Functional Theory (DFT) to evaluate 100 flavonoids against four key breast cancer targets which are; ERα, PI3K, HER2, and EGFR. Comparative docking with five reference drugs (Alpelisib, Buparlisib, Lapatinib, Gefitinib, and Afatinib) identified nine flavonoids; Sphaerobioside, Avicularin, Nicotiflorin, Myricetin, Quercitrin, Rutin, Isoquercetin, Didymin, and Robinin as promising candidates with favorable binding affinities and stable receptor interactions. MM/GBSA results supported the docking outcomes, revealing strong binding stability across multiple targets. ADMET predictions suggested acceptable pharmacokinetic and safety profiles for several compounds, while DFT analysis provided insight into their electronic stability and reactivity. Collectively, these findings highlight the multitarget inhibitory potential of selected flavonoids and demonstrate how integrated computational profiling can accelerate the discovery and optimization of natural product-based anticancer agents.

The online version contains supplementary material available at 10.1007/s40203-025-00489-0.