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Computational approaches to exploring "chemical universes", i.e., very largesets, potentially infinite sets of compounds that can be constructed by aprescribed collection of reaction mechanisms, in practice suffer from acombinatorial explosion. It quickly becomes impossible to test, for all pairsof compounds in a rapidly growing network, whether they can react with eachother. More sophisticated and efficient strategies are therefore required toconstruct very large chemical reaction networks.  Undirected labeled graphs and graph rewriting are natural models of chemicalcompounds and chemical reactions. Borrowing the idea of partial evaluation fromfunctional programming, we introduce partial applications of rewrite rules.Binding substrate to rules increases the number of rules but drastically prunesthe substrate sets to which it might match, resulting in dramatically reducedresource requirements. At the same time, exploration strategies can be guided,e.g. based on restrictions on the product molecules to avoid the explicitenumeration of very unlikely compounds. To this end we introduce here a genericframework for the specification of exploration strategies in graph-rewritingsystems. Using key examples of complex chemical networks from sugar chemistryand the realm of metabolic networks we demonstrate the feasibility of ahigh-level strategy framework.  The ideas presented here can not only be used for a strategy-based chemicalspace exploration that has close correspondence of experimental results, butare much more general. In particular, the framework can be used to emulatehigher-level transformation models such as illustrated in a small puzzle game.

Generic Strategies for Chemical Space Exploration