  Graph transformation systems have the potential to be realistic models of
chemistry, provided a comprehensive collection of reaction rules can be
extracted from the body of chemical knowledge. A first key step for rule
learning is the computation of atom-atom mappings, i.e., the atom-wise
correspondence between products and educts of all published chemical reactions.
This can be phrased as a maximum common edge subgraph problem with the
constraint that transition states must have cyclic structure. We describe a
search tree method well suited for small edit distance and an integer linear
program best suited for general instances and demonstrate that it is feasible
to compute atom-atom maps at large scales using a manually curated database of
biochemical reactions as an example. In this context we address the network
completion problem.
