Abstract: Metabolic databases contain information about thousands of small molecules and reactions, which can be represented as networks. In the context of metabolic reconstruction, pathways can be inferred by searching optimal paths in such networks. A recurrent problem is the presence of pool metabolites (e.g. water, energy carriers, co-factors), which are connected to hundreds of reactions, thus establishing irrelevant shortcuts between nodes of the network. One solution to this problem relies on weighted networks to penalize highly connected compounds. A more refined solution takes the chemical structure of reactants into account in order to differentiate between side and main compounds of a reaction. Thanks to an intensive annotation effort at KEGG, decompositions of reactions into reactant pairs (RPAIR) categorized by their role (main, trans, cofac, ligase, leave) are now available. The goal of this paper is to evaluate the impact of the RPAIR data on path finding in metabolic networks. To this end, we measure the impact of different parameters concerning the construction of the metabolic network: mapping of reactions and reactant pairs onto a graph, use of selected categories of reactant pairs, weighting schemes for compounds and reactions, removal of highly connected metabolites and reaction directionality. In total, we tested 104 combinations of parameters and identified their optimal values for path finding on the basis of 55 reference pathways from three organisms. The best-performing metabolic network combines the biochemical knowledge encoded by KEGG/RPAIR with a weighting scheme penalizing highly connected compounds. With this network, we could recover reference pathways from Escherichia coli with an average accuracy of 93% (32 pathways), from Saccharomyces cerevisiae with 66% (11 pathways) and from human with 70% (12 pathways). Our path finding approach is available as part of the Network Analysis Tools (NeAT): http://rsat.ulb.ac.be/neat/.