Although network-based models have achieved significant breakthroughs, their application is still restricted by the complex intricacies of human metabolism ( ). The com-plexity originates from the extensive interconnection of biochemical events and regulatory mechanisms that govern metabolic pathways ( ). Existing models frequently encounter difficulties in comprehensively capturing the dynamic interactions and metabolic fluxes within this intricate system, which presents obstacles in precisely forecasting drug metabolism, toxicity, and efficacy ( ). Continuous progress in data integration, modelling approaches, and computational resources is necessary to overcome these restrictions and attain more extensive and dependable forecasts in drug development and personalised medicine.