In the following sections, we present a detailed analysis of the model's performance across different embedding approaches and an expert chemical evaluation of predicted routes, demonstrating both the technical capabilities and practical applicability of our scoring system. Beyond raw distance prediction, we also evaluated the model's ranking capability in terms of the model's ability to identify the best routes for a novel molecule. We compare the model's predicted top-1 route against the true optimal route for each molecule in the test set. Specifically, we ranked all possible routes for each molecule using both predicted and true distances, then calculated the proportion of cases where the model's predicted best route matched the actual optimal route. As shown in the top-1 ranking accuracy metric in Table , SDF still performs the best with the top-1 ranking accuracy of 0.603 ± 0.007. To visualize ranking accuracy across different distance ranges, we present the ratio of the true top-1 route in predicted top-k routes in the second row of Figure . Further detailed in Figure , where we examined the proportion of reference routes present in the top 20 predicted routes. The SDF embedding showed superior performance in this aspect as well, with approximately 95% of the best routes identified within the top-20 predictions. This significantly outperforms both DRFP and RXNFP embeddings, which achieved around 90% identification rates. 3UHGLFWHG5DQN 5DWLR 6') '5)3 5;1)3 1RHPEHGGLQJV