The main advantage of the RegioSQM20 approach is that it may offer an accuracy similar to RegioML based on a much smaller training set. Indeed a LightGBM model trained on the same 532 reactions used to develop RegioSQM20 results in MCC values around 0.5 for both the test and out-of-sample set. While the precision is quite good for this model, the recall is less than 50% due to a large proportion of false negatives. Thus, in cases where little experimental data is available physics-based methods like RegioSQM20 is likely to outperform ML based methods, even if the latter rely on quantum descriptors such as atomic charges.