The application of machine learning for predicting the stereoselectivity of glycosylations has previously been investigated. Although most applications have been to biological processes , prediction of anomeric ratios in chemical glycosylations has been pursued by both Moon et. al and Chang et. al in 2021. A brief comparison of Moon et. al, Chang et. al, and our work can be seen in Figure . Both studies built their machine learning models on in-house generated data, limiting the diversity of training data and encompassing only 268 and 139 reactions, respectively. Moon et al. train a random forest (RF) model using 11 different descriptors including the exposed surface area of the nucleophile, calculated C chemical shift of the anomeric carbon, HOMO energy of the conjugate base of the acid catalyst, minimum and maximum electrostatic potentials of the solvent, among others. Chang et al. first examine the relationship between the anomeric ratio of glycosylation products and two factors: the acceptor nucleophilic constant (Aka) and the relative reactivity value (RRV) relative to the donor reactivity. They also train a random forest model to predict the stereoselectivity in the reaction between phenyl thioglycosides and various alcohols in DCM. They use 20 descriptors including the RRV, Aka, C-2 modification, and C4 stereochemistry. Furthermore, in 2020, Pesciullesi et al. reported a fine-tuned natural language processing-inspired model called the Carbohydrate Transformer to predict the regio-and stereoselectivity of glycosylations, thus only the major product, not the ratio. Our goal was to design a model that could both predict the major product in glycosylations and the anomeric ratio trained on a literature dataset with high diversity that precludes manual analysis. To the best of our knowledge, the model presented in this work is the first model for predicting the anomeric ratio of glycosylations trained on a dataset and chemical space of this size. Herein we show the following: (1) It is possible to train a machine learning model to predict anomeric selectivity using literature data. (2) Machine learning models identify trends in the data similar to widely accepted chemical glycosylation trends. (3) Looking at glycosylations through the lens of machine learning enables us to algorithmically discover new principles of stereoselectivity supported by literature data.