At 2004, the department of energy of USA announced high value-added building blocks from biomass. All the reported top value-added building blocks were carbon monomers while the synthetic pathways in the metabolic network need to be developed or found for commercial purposes. To synthesize those carbon monomers with exiting sources, it is necessary to predict the promising enzymes to catalyze novel reactions producing desired products. We designed a system framework which predicts promising enzyme candidates for desired carbon monomers. Since a biological reaction occurs when substrates are attached to an enzyme, the similarity of binding site and reaction rule can qualify whether an enzyme would catalyzes the reaction or not. Through the qualification step, numerous enzyme candidates are identified. All the identified candidates are structurally meaningful; however, more promising enzymes should be noticed for the experimentations. Thus, the quantifying methodology is consequently used to evaluate qualified enzyme candidates. By five factors, such as chemical similarity, binding site covalence, thermodynamic favorability, pathway distance, and organism specificity, qualified enzymes are ordered by their quantified values. If an enzyme gets higher quantified value than others, the enzyme is called as the most promising enzyme candidate. In the system framework, synthetic pathways for any desired carbon monomers can be suggested. It is expected to contribute to speeding up the research cycle of high value-added chemicals from biomass.