Journal of Complementary and Alternative Medical Research

This study evaluates the anticancer potential of phytochemical constituents derived from Bougainvillea glabra, with a specific focus on Gallic acid derivatives synthesized using aromatic aldehydes and aromatic amines through an in silico molecular docking strategy. The shade-dried and powdered plant material was subjected to maceration using a suitable solvent to extract bioactive constituents. After solvent removal, the isolated phytoconstituents were identified and characterized using standard phytochemical screening and spectral analyses, including FT-IR, UV–Visible, and NMR techniques. The structurally characterized compounds were subsequently used for computational evaluation to assess their potential anticancer activity. Molecular docking analysis revealed that the synthesized derivatives exhibited significantly improved binding affinity and hydrogen bond interactions compared to the co-crystallized ligand and the reference anticancer drug Adriamycin, indicating enhanced stability within the active sites of the target proteins. Docking simulations were carried out using MolegroVirtual Docker (MVD) version 6.0 against cancer-related target proteins with PDB ID 5T4B (also known as CD26, is a multifunctional serine protease involved in cell signalling). In addition, physicochemical properties and pharmacokinetic parameters were assessed using Molinspiration and the PreADMET online server to predict drug-likeness and ADMET profiles. Among the tested compounds, the aromatic amine derivative of gallic acid (AA11) demonstrated the highest binding affinity toward PDB ID 5T4B, with a MolDock score of −117.059, forming stable hydrogen bond interactions with key amino acid residues, including Asp545, Trp629, Trp563, Ala564, Trp627, Asn562, and Lys554..Overall, this integrative computational approach underscores the effectiveness of combining phytochemical analysis, molecular docking, and ADMET prediction for the identification of promising plant-derived anticancer candidates, while minimising cost, time, and reliance on experimental animal models during early-stage drug discovery.