Abstracting complex data sets into networks is a well-established approach in trying to study trends underlying complex data. Chemical data has, for example, been converted into networks and used for planning of synthesis routes or as an analytical tool to study the structure of chemical knowledge . This lead us to formulate a hypothesis for algorithmic use of chemical knowledge, which has not been explored in chemical science to date: the structure of chemical networks by itself contains information about chemistry 'rules' and appropriate mathematical treatment of the structure of chemical networks can give insights into yet undiscovered phenomena, such as new reactions without being adversely affected by poor or incomplete reaction data, such as errors in reporting, propagated into the databases or errors in abstracting the data from primary publications into the databases.