Integrative Computational Analysis of Dragon Fruit Phytochemicals Targeting Cancer, Diabetes, and Inflammatory Pathways

• 51 compounds mapped across disease targets and signaling pathways • Systems-level insight predicts therapeutic targets of dragon fruit phytochemicals • Key targets and PI3K/AKT, MAPK pathways identified • ADMET, docking, and MD simulation validate bioavailable lead compounds Dragon fruit ( Hylocereus spp.) is a rich source of bioactive phytochemicals with demonstrated pharmacological potential. The phytoconstituents can be extracted from the flesh, peel, and seeds of the fruit. This application-driven study employs an integrated network pharmacology approach to generate hypotheses regarding the molecular mechanisms by which dragon fruit compounds may be potential candidates to treat cancer and diabetes. Initially, targets related to dragon fruit compounds and associated diseases were identified from various databases, and overlapping targets were then determined. Following this, analyses including PPI network analysis, GO enrichment, KEGG pathway enrichment, and molecular docking were performed to explore the potential mechanisms by which these compounds may aid in the treatment of cancer and diabetes. Fifty-one compounds from dragon fruit were found from previously reported studies and screened for ADMET properties. Ascorbic acid, kaempferol, catechin, and epicatechin were selected based on their good properties. 125 common compound-cancer and 142 common compound-diabetes targets were identified. Analysis of the protein-protein interaction network showed that most connected hub genes were IL6, AKT1, EGFR, SRC, STAT3, ESR1, JUN, PRARG, MMP9, and PTGS2 for both diseases. EGFR tyrosine kinase inhibitor resistance was the most prominent pathway. Molecular docking with the top three proteins revealed the most significant binding interactions with kaempferol and epicatechin. Subsequent MD simulations and free-binding energy studies revealed that catechin may exhibit stable conformational dynamics and favorable binding affinity in modeled complexes with EGFR and IL-6. These computational predictions provide a hypothesis-generating framework that may guide future experimental studies investigating dragon fruit phytochemicals in chronic disease contexts.