The interplay of the rule-based and MCS-based methods described in Section 2.4 results in a satisfactory performance of the SynRBL framework. Fig. shows that the tool reaches success rates between 89.8% (Golden) and 100% (Urnd) at accuracies between 90.8% (Golden) to 99.4% (Urnd). More detailed values are listed in Supplementary Table . The significantly lower performance metrics observed within the Golden dataset can be attributed to the inherent complexity of the its reactions, which also include the presence of solvents and catalysts. These elements introduced additional variables into the molecular alignment process, thus posing significant challenges to the predictive capabilities of this framework. In addition, we evaluated the computational efficiency of our methods, observing an average processing time of 46 seconds per 1000 reactions on an average workstation where one-third of the reactions were solved by MCS. In our comparative analysis, our method surpassed the current state-of-the-art, ChemMLM , demonstrating superior performance in both success rate and accuracy. The reported outcomes for ChemMLM showed a success rate fluctuating between 4.1% to 42.7% on the USPTO dataset. In contrast, SynRBL demonstrates a remarkable success rate of 99% or higher on the same dataset. Moreover, while the accuracy of ChemMLM varied widely (from 100% for shorter SMILES strings to a mere 8.2% for larger molecules). SynRBL's accuracy remains robust, largely unaffected by molecular size, and consistently exceeds 98% across the USPTO dataset.