We presented an efficient method to suggest near-optimal biochemical pathway via data mining from organic chemistry and synthetic biology datasets, and reinforcement learning decision making. With this method, we also proved that, in overall, hybridising chemical and biological reactions to plan synthetic pathways was better than conventional organic synthesis by 3.4% with respect of the synthesis of all target molecules in the molecular space, due to advantages of synthetic biology to improve redox efficiency and enable synthetic shortcuts for the reaction routes. This conclusion was justified by using atom economy, numbers of reactions steps, and price of building blocks as key criteria to quantify retrosynthesis performance, and applying a comprehensive list of target molecules for the comparison of the organic chemical, synthetic biological and hybrid reactions pools. We could especially benefit from the well-trained policy model to plan the synthetic routes of a set of drug molecules. The case study of certain drug molecules indicates synthesis would be significantly eased with the help of synthetic biology reactions. This methodology could be further extended to mine more comprehensive reaction data to further understand the true costs of making biological reactions, which would make it possible to plan reaction routes with more confidence.