The computational expense of the physics-based methods can be mitigated by using them to generate synthetic data for the machine learning model. Indeed, a LightGBM classifier trained on RegioSQM20 predictions for the large training set of 58K reaction centers offers the same performance as RegioSQM20 for the test set. Of course, the performance is worse for the out-of-sample set just like for RegioML, but the training dataset can now easily be expanded to ensure a better coverage of chemical space. Furthermore, since RegioSQM20 is not used to offer real-time predictions to a user, more accurate and computationally expensive methods can be explored. For example, the precision of RegioSQM20 can be increased by 6% by using PBEh-3c single point calculations to compute the proton affinities -an increase that is reflected in the corresponding ML model. The overall performance of RegioSQM20 PBEh-3c is now identical to the RegioML for the out-of-sample dataset, with MCC values of 0.72.