The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases and apply the knowledge to predict reactions to synthesise new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is clearly a need for a method to rapidly predict reactions for synthesising new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated reactant mapping, which are often hard to obtain accurately. In this talk, I will describe a robust method based on graph mining, to predict a series of biochemical transformations, which can convert between two (even previously unseen) molecules. We mine the reaction database and store reaction centres and signatures in a reaction rule network. Such a novel representation enables us to rapidly predict pathways. We also propose a heuristic that predominantly recovers natural biosynthetic pathways from amongst hundreds of alternatives, through a directed search of the reaction rule network, enabling us to provide a reliable ranking of pathways. Our approach scales well, even to databases with >100,000 reactions.