Metabolic pathways collectively define the biochemical repertory on an organism exposing the steps of its production. In silico metabolic pathways have been reconstructed using a wide range of computational methods. The reconstructed metabolic pathways can vary in some aspects, among which, in the context of this work, it is relevant to remark the data structure and granularity of biochemical details. Inferring chemical reactions using graph grammar rules is a method that exposes the initial, intermediates, and final products by modeling pathways over graphs and hypergraphs. Plant monoterpenes are volatile compounds with applications in industry, biotechnology, and medicine. They also play a vital ecological role. The last enzymatic reaction in the plant monoterpenes production chain is plentiful of possibilities due to the promiscuous nature of the terpene synthases (TPS), a case in which the application of inferring chemical reactions using graph grammar rules is suitable. In this work, we designed graph grammar rules that express cyclization reactions catalyzed by plant monoterpene synthases. As a result, it was generated a reachable chemical space of potential plant monoterpene blend, which can be computationally exploitable. In addition, these graph grammar rules were added to the 2Path, and a graphical interface was provided to aid the simulation code outlining.