Comparing the total number of correctly identified molecules, we observed the following: For our simulated data, the model identified 34 out of 34 (100%) molecules with correct starting structures, and 29 out of 34 (85%) with modified starting structures. With ACD simulated data, 31 out of 34 (94%) molecules were found using correct starting structures, compared to 22 out of 34 (65%) with modified starting structures. For experimental data, the model identified 31 out of 34 (94%) molecules correctly with accurate initial guesses, versus 19 out of 34 (56%) with modified starting points. These results demonstrate that while there is a decrease in performance when starting with modified structures, the model still maintains a substantial ability to identify correct molecules. This robustness is valuable in real-world applications where initial structural assumptions may not always be entirely accurate, showcasing the Mol2Mol network's ability to explore relevant chemical space and allow the MMT model to overcome initial inaccuracies in many cases. A figure showing all the wrong starting guesses of the molecules is presented in Supplementary Figure .