Identification and characterization of anti-epileptic compounds from Papaver somniferum using quantification techniques (GC-MS, FTIR), integrated network pharmacology, molecular docking, and molecular dynamics simulations

Epilepsy is a common neurological condition identified by repetitive seizures that affect the overall quality of life. Existing anti-epileptic drugs have undesirable side effects, necessitating safer alternatives. This study develops an integrated computational framework to discover potential anti-epileptic leads from Papaver somniferum (opium poppy). Literature and databases were mined to compile all chemicals from Papaver somniferum. PubChem provided structural data, and compounds satisfying drug-likeness and bioavailability criteria were selected. GeneCards, DisGeNET and SwissTargetPrediction identified 344 target genes of the compounds and common targets with epilepsy. Network pharmacology analyses were performed. Cytoscape constructed a compound-target network comprising 5 active constituents and 22 shared targets. Degree distributions revealed molecular interactions. STRING elucidated target connectivity. Hub targets were identified using CytoHubba. GO and KEGG enrichment on 123 targets recognized biological roles and pathways. DAVID and Hiplot characterized functional annotations. Cytoscape visualized a compound-target-pathway association network involving targets, pathways, and compounds related to epilepsy. GC-MS identified 25 compounds in the Papaver somniferum extract. FTIR characterized functional groups. Molecular docking scored compound affinities for 10 targets. Autodock Vina docked 15 constituents into binding pockets. Interactions were validated using Desmond MD simulations of IL6 with scoulerine over 100 ns, assessing RMSD, RMSF, interactions. RMSD/RMSF plots and histograms characterized protein/ligand stability and flexibility. This integrative Insilico and Invitro framework facilitates prioritizing Papaver somniferum constituents for epilepsy. Network analyses provided systems-level understanding of multi-target mechanisms. Molecular modeling established structure-activity relationships, validating predicted interactions. Compounds with good ADMET profiles, network centrality, docking scores, and stable simulations emerge as candidates warranting further examination for safer anti-epileptic therapy.