Metabolic modeling is essential for understanding the mechanistic bases of cellular metabolism in various organisms, from microbes to humans, and the design of fitter microbial strains. Metabolic networks focus on the overall fluxes through biochemical reactions that implicitly rely on several biochemical processes, such as active or diffusive uptake (or export) of nutrients (or metabolites), enzymatic turnover of metabolites, and metal-cofactor enzyme interactions. Despite independent progress in biomolecular simulations, they have yet to be integrated to inform metabolic models. We explore the evolution of computational metabolic modeling approaches, starting with flux balance analysis, dynamic, kinetic delineations of metabolic shifts in single organisms within cells and across tissues, and mutually informing, community-level modeling frameworks and provide a narrative to tie in biomolecular simulations and machine learning predictions to usher the new phase of structure-guided synthetic biology applications. These additions and prospective novel ones are likely to open hitherto untapped paradigms for optimizing/understanding metabolic pathways toward improving bioproduction of protein and small molecule products with downstream applications in health, environment, energy, and sustainability.