Abstract
Snakebite envenomation continues to be a major issue of public health which is mainly the case in tropical areas such as India where Naja naja is the main cause of death and diseases related to snakebite. Traditional antivenoms have certain defects, among which poor effectiveness against local tissue injuries and the variability of snake venom are the most significant. This study investigates the antivenom potential of phytochemicals from Canthium coromandelicum, a traditionally used medicinal plant, through a comprehensive in silico pipeline. Methanolic extracts of leaf were subjected to HRLC-MS profiling, identifying 69 bioactive compounds. A machine learning framework (GraphDTA with GINConvNet) was employed for virtual screening of these phytochemicals against key N. naja venom proteins (1CVO, 1MF4, 1NTN, 2CTX, and 7QHI), predicting binding affinities based on graph-based molecular representations. Top candidates were further evaluated via molecular docking, molecular dynamics (MD) simulations, and density functional theory (DFT) analyses to elucidate their binding stability, conformational dynamics, and electronic reactivity. Key phytocompounds, including 8-C-Galactosylluteolin, Araliasaponin V, Saponin D, Quinic acid, and Quercetin 3,7-dirhamnoside, demonstrated strong binding affinity (docking scores: -5.484 kcal/mol to - 9.777 kcal/mol), stability (RMSD < 1.4 Å) and reactivity against venom targets. Additionally, ADMET and toxicity profiling suggested favorable pharmacokinetic properties for several compounds, though nephrotoxicity and immunotoxicity risks were identified. Inclusively, this integrative computational approach highlights promising natural leads for the development of plant-based adjuncts or alternatives to conventional antivenom therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00527-x.