Recent advancements in Artificial Intelligence (AI) and data-driven methods have markedly accelerated the molecular design of drug candidates and materials. The development of recipes to construct a target compound from available starting materials by orchestrating a sequence of chemically viable reaction steps, i.e., synthesis planning thus also is increasingly treated as a computational problem. Traditionally, it is most commonly approached by retrosynthetic analysis, formalized by E. J. in the 1960s as the recursive decomposition of the product considering the reverse of synthetic reactions. This calls for the exploration of a vast array of potential chemical reactions that have not yet been observed but are analogous to known chemical transformations. In other words, modern synthesis planning is based on reaction patterns that summarize the accumulated knowledge about feasible chemical reactions. Here, we describe a computational framework, SynTemp, designed to extract such reaction patterns from large-scale repositories of chemical reactions and to make them available in the form of explicit transformation rules.