Integrated computational and experimental identification of Aurora A kinase inhibitors for breast cancer therapy

The present study aimed to identify potential Aurora A kinase (AURKA) inhibitors using an integrated computational and experimental approach. A total of 281 curated AURKA inhibitors from the ChEMBL database, with IC₅₀ ≤ 1 nM, were analysed using physicochemical descriptors and clustering techniques, with K-means showing the best performance (0.504). The compounds were grouped into three clusters and used to generate a pharmacophore model. Hydrogen-bond acceptors, donors, hydrophobicity, and aromatic rings were identified as key pharmacophoric features. Among the generated models, ADDRR and AAADR exhibited the best PhaseHypoScore and ROC values. These validated models screened approximately 800 FDA-approved drugs from the ZINC database. Molecular docking found that rucaparib (ZINC000000025958; C1), riboflavin (ZINC000002036848; C2), and imiquimod (ZINC000019632912; C3) were effective AURKA inhibitors. Molecular dynamics (MD) simulations and MM-GBSA analysis indicated that the C1 protein-ligand complex maintained structural integrity and favourable binding energetics throughout the simulation trajectory. Further, in vitro MTT assays demonstrated significant dose-dependent anticancer activity and reduced AURKA expression in MCF-7 cells, highlighting rucaparib's potential for breast cancer therapy.