Despite the interest in chemoinformatics to combine multiple datasets to have comprehensive understanding of the chemical space, to the best of our knowledge, none has worked on combining organic chemistry and synthetic biology databases and understand the benefits from both in retrosynthesis planning. In this work, we mined a section of historical reactions from Reaxys ® database, 31 and all metabolic reactions from Kyoto Encyclopedia of Genes and Genomes (KEGG), which is an open-source manually curated bioinformatic library. We compared the influence of the presence of organic synthesis and synthetic biology past reaction data in a dataset used for identification of retrosynthesis pathways of a curated set of drug molecules, which were believed to be difficult to synthesise. To evaluate the identified routes we used atom economy, the number of reaction steps and price of molecular building blocks as key quantifiable performance criteria. Reinforcement learning method from Schreck et al. was adapted to build policy models to guide the search for retrosynthesis pathways. Different from other CASP tools, the synthetic pathways from this method were not assembled from predicted reactions (i.e., using reaction template or the algorithm generated reactions ), but using historical published data. This reduced the operational uncertainty over the identified paths to enable us to focus on the key research question of this work -how much added value could synthetic biology bring to synthetic organic chemistry in multi-step syntheses?