Several algorithmic methods have been developed for generalizable and quantifiable synthesis evaluation. Schwaller et al. introduced four key metrics-coverage, class diversity, round-trip accuracy, and Jensen-Shannon divergence-to assess single-step retrosynthesis models. Lin et al. used a heuristic scoring function based on the F1 score of common scaffolds between target and starting molecules, while Badowsky et al. developed a neural network-based scoring model that predicts reaction probabilities from Morgan fingerprints. These methods, however, focus only on singlestep evaluations without considering route-level information. Mo et al. argued that heuristic scoring may overlook creative retrosynthesis routes and developed a model to assess the strategic viability of multi-step synthesis pathways using an LSTM encoder. Their model ranks routes based on strategic value, focusing on pathway-level relationships. In contrast, our work incorporates both single-step reaction assessments and overall route properties. Additionally, their model was trained as a ranking task where absolute score values are less meaningful, whereas our scoring model aims to provide a universal and interpretable assessment standard based on score levels.