Decoding the anticancer and biofilm-inhibiting efficacy of

Introduction: Methods: The study employs GNINA, a deep learning-based docking tool, to evaluate molecular interactions. This work integrates machine learning and molecular modeling methodologies, highlighting the potential of informatics-driven strategies to expedite the discovery of novel plant-based therapies. Results and Discussion: Fluorescence microscopy demonstrated that ADEE effectively inhibited biofilm formation and reduced cell viability at a concentration of 1.56 μg/mL. These findings suggest that ADEE disrupts quorum-sensing signaling pathways and compromises the structural integrity of the biofilm matrix. Further assessments of cytotoxicity revealed a dose-dependent reduction in cancer cell viability, highlighting the potent anticancer properties of ADEE. The study also confirmed the pro-apoptotic effects of ADEE through Hoechst and AO/EB staining techniques. Validation utilizing GNINA-based deep learning techniques demonstrated an enhanced binding affinity and pose stability of compounds derived from ADEE. Molecular dynamics simulations provided insights into the interactions of ADEE with pqsA and CK2, showing more favorable binding characteristics compared to the reference inhibitor. PCA/FEL analyses indicated stable conformations with significant interactions at critical residues. In summary, the phytocompounds identified in ADEE demonstrated enhanced binding affinity and structural stability, indicating promising therapeutic potential for targeting QS-regulated biofilm development and serving as potential anticancer agents.