Growth-coupled production, in which cell growth forces the production of target metabolites, plays an essential role in the production of substances by microorganisms. The strains are first designed using computational simulation and then validated by biological experiments. In the simulations, gene-deletion strategies are often necessary because many metabolites are not produced in the natural state of the microorganisms. However, such information is not available for many metabolites owing to the requirement of heavy computation, especially when many gene deletions are required for genome-scale models. A database for such information will be helpful. However, developing such a database is not straightforward because heavy computation and the existence of replaceable genes render difficulty in efficient enumeration. In this study, the author developed efficient methods for enumerating minimal and maximal gene-deletion strategies and a web-based database system. MetNetComp provides information on 1) a total of 85,611 gene-deletion strategies excluding apparent duplicate counting for replaceable genes for 1,735 target metabolites, 11 constraint-based models, and 10 species; 2) necessary substrates and products in the process; and 3) reaction rates that can be used for visualization. MetNetComp is helpful for strain design and for new research paradigms using machine learning.