In conclusion, we have developed interpretable machine learning models for predicting anomeric selectivities using literature data. The models trained to predict the major anomeric product of glycosylations have been shown to outperform human experts. The models have been combined to create the "GlycoPredictor", a publicly available tool for predicting anomeric selectivities in glycosylation reactions. By analyzing the major anomerpredicting models and comparing them to human experts, it was possible to get a better understanding of the rules and the hierarchy of the rules dictating the stereochemical outcome of chemical glycosylation according to historical reactivity trends. It is well known that most glycosylations tend to give the 1,2-trans glycoside, which is also the trend identified by the decision tree algorithm, with most of the decision tree nodes being based on effects that counteract this trend. However, it seems that assuming β-selectivity for glycosyl donors with an equatorial C-2 if the C-2 substituent is not an azido group (ML decision tree), rather than assuming α-selectivity, if NGP is not possible (expert decision trees), increases the accuracy of the predictions. Furthermore, by analysis of the deeper branches in the ML decision tree, two novel trends were discovered which will be investigated in future work.