For carbon compounds, we consider a maximum common subgraph (MCS) problem. This family of combinatorial optimization problems plays an important role in structural comparisons in chemistry and biology . It underlies similarity searches vital to the preliminary phases of drug discovery, offering metrics for molecular structure similarity based on MCS dimensions, in alignment with the principle of similar properties . Beyond similarity assessment, MCS analysis is integral to clustering processes , the identification of matched molecular pairs , reaction mapping , and the alignment of molecules . MCS problems come in two flavors, both of which are NP-hard . These two flavors are the maximum common induced subgraph (MCIS), which focuses on atom count, and the maximum common edge subgraph (MCES), which focuses on edge count. They give notable differences in the analyses of dissimilar molecules . Our MCS-based approach targets carbon compound gaps and reactions beyond the rule-based method's scope by aligning reactants and products to pinpoint and merge non-aligned segments, generating missing compounds. An iterative technique proceeding by overlapping molecules one at a time and isolating non-overlapping regions for efficient alignment in subsequent rounds is introduced to reduce computational costs.