We finetuned and evaluated our model following the same protocols as before, excluding from the finetuning set the 61 experimentally detected test asset impurities, and we compared this model with the model finetuned on historical in-house impurities described in the previous section. Both models correctly predicted the same 46 (78.0%) impurities in a de novo setting. For fragment completion, finetuning on simulated data appeared to result in better performance, although the small dataset size of only 61 impurities caused some fluctuations (Figure ). Nonetheless, when considering all fragments altogether the model finetuned on simulated data had 5.6% higher accuracy (88.7% vs 83.1%). This shows that an entirely in silico impurity simulation approach of process impurities and their MS/MS spectra can be used to significantly boost elucidation accuracy in absence of relevant experimental data for finetuning.