This paper introduces a scoring metric and establishes an evaluation criterion for chemical synthesis. We presented a scoring network for multi-step synthesis route design and selection that is trained to predict the estimated distance or similarity to the experimentally demonstrated routes. The scoring model was set to take advantage of different factors of the synthesis route evaluation including the complexity of intermediates or cost to perform the reaction, as well as the reaction molecular representation and thus implicitly learned reaction feasibility. We also performed validation experiments on the model using different methods of embedding to assess its prediction power. The top-1 ranking accuracy reached 60.2% with a prediction error of 2.72 (RMSE) for SDF embedding. Through the experiment, we have demonstrated that when using the SDF embedding method, the model exhibited the best prediction power while taking relatively little time to generate encodings. SDF was also shown to be the most stable among the tested methods, having very low variation. This improved the performance of the model making it more robust and reliable. Based on expert chemists' analysis, we proposed an evaluation criterion that combines both the score and route length metrics, allowing us to classify route scores from the model into three distinct categories. When compared with the chemists' ratings, our model demonstrated strong predictive accuracy, aligning with expert assessments 74 % of the time. By embedding expert knowledge, the model captured the implicit evaluation standards prevalent in the chemistry community, accounting for both reaction-level intricacies and complex route-level preferences. It is important to note that our route scoring model when combined with AIZynthFinder as a route generator significantly outperformed the state-of-the-art DirectMultiStep retrosynthesis tool with the top-1 ranking accuracy of 38 % . When compared with the state-of-the-art SOA framework , our system is better in suggesting the best or the most strategically preferred route (79% vs 90% accuracy in favor of our framework). Importantly, it offers firstin-class multi-step route quality and feasibility assessment based on a combination of machine learning and human expertise with the Pearson correlation coefficient of 0.92 between the human expert and deep learning model ranking.