Identification of Anti-Oxidative, Anti-Mitotic, and Anti-Angiogenic Compounds from Triticum aestivum via AI-Based Network Pharmacology and In Vitro Assays

Abstract

This study integrates AI-guided network pharmacology with multi-scale experimental validation (in vitro and ex vivo) to elucidate the therapeutic potential of small molecules from Triticum aestivum (wheatgrass) hexane extract. AI-assisted compound screening, molecular docking, and pathway enrichment identified bioactive constituents with predicted interactions against breast cancer-associated targets, including SRC, AKT1, EGFR, TNF, and IL-6. Network analysis revealed dual modulation of oxidative stress response pathways and mitotic spindle assembly checkpoints, alongside potential interference with pro angiogenic signaling. Experimental validation confirmed these predictions: the extract exhibited significant free radical scavenging activity (DPPH assay) and reduced oxidative DNA damage, as demonstrated by the COMET assay in relevant cell models. Antimitotic activity was evident through in vitro root tip assays and ex vivo CAM (chorioallantoic membrane) assays, which also revealed suppression of angiogenesis. Ex vivo toxicity assessment using the isolated chicken eye test showed a favorable safety profile, and brine shrimp lethality assays indicated low systemic toxicity. These findings demonstrate that small molecules from a wheatgrass hexane extract exert their anticancer potential via a synergistic mechanism, attenuating oxidative stress, inducing mitotic arrest, and inhibiting angiogenesis, supported by AI-driven target prediction and validated through comprehensive biological models. This combined computational experimental framework highlights the value of artificial intelligence in accelerating the discovery and mechanistic understanding of phytochemical-based therapeutics.