Conventional chemical products development starts from raw materials, such as petrochemical feedstocks or bio-based resources. It involves chemical synthesis steps to obtain the desired target intermediates (industrial chemicals) and functional molecules. In contrast, synthetic biology is concerned with molecules that are intermediates at various stages of metabolic pathways, and could lead to production of target substances in cell-based bioreactors. Compared to organic synthetic paths, the bio-synthetic paths typically have higher redox efficiency and moderate reaction conditions; both factors are desirable from the point of view of reducing environmental impact of industrial processes (we do not discuss here the well-known issues of complexity of product separation from bio-processes). An addition of synthetic biology into computer-assisted synthesis planning (CASP) of pharmaceuticals and industrial chemicals certainly opens up opportunities for more efficient chemical production. Recently we generated a hybrid reaction network to bridge the domains of organic synthesis and synthetic biology. From the hybrid network, it is clear that the biological reaction space is very sparse compared with the organic chemical network, involving only 0.35% of the total reactions in the combined dataset. Similarly, Levin et al. and Sankaranarayanan et al. also published methods of merging enzymatic and synthetic chemistry to guide CASP. In Levin et al.'s work, reaction templates were summarised from the reaction transformations, and hybrid synthetic pathways were assembled from reaction templates. Among the 171 thousand reaction templates, only 4.6% were from enzymatic templates. In Sankaranarayanan et al. 's work, an open-sourced CASP was used to compute organic synthetic pathway for target molecules, and then expert encoded metabolic reaction rules were used to replace as many reaction steps as possible. These practical implementations of CASP based on both biocatalytic and chemical synthesis are hindered by the limited amount of the biochemical reactions data.