Pharmaco-informatics based prediction of anti-inflammatory targets modulated by Rhizophora apiculata

Rhizophora apiculata Blume (RAP; Rhizophoraceae), a marine true mangrove, exhibits notable immunomodulatory potential, though its molecular mechanisms remain poorly understood. This study employed a pharmacoinformatics-based approach to predict the mechanism of its anti-inflammatory potential. Literature mining and GC-MS profiling identified 10 drug-like phytochemicals. Target prediction using SwissTargetPrediction, SuperPred, and PharmMapper, integrated with inflammation-associated gene databases, predicted 70 overlapping inflammation-related targets. Protein-protein interaction analysis of these targets yielded a highly enriched network (56 nodes, 190 edges; PPI enrichment p < 1.0e^-16). Functional enrichment analyses indicated strong associations with immune and inflammatory processes, including leukocyte migration, chemotaxis, and lipopolysaccharide response, alongside key inflammatory signalling pathways. Site-specific molecular docking of RAP phytochemicals against the topologically prominent targets EGFR, COX2, JAK2 and MAPK14 revealed favourable binding affinities. Notably, glycosin (5), ( +)-dihydroquercetin (1), and isolariciresinol (2) were predicted to bind stably to druggable anti-inflammatory targets. Molecular dynamics simulations further supported the stability of these complexes, particularly COX2 + 5, JAK2 + 1, and MAPK14 + 2. These findings provide testable mechanistic hypotheses for the anti-inflammatory effects of R. apiculata.

The online version contains supplementary material available at 10.1007/s40203-026-00622-7.