Computational methods to predict the nature and outcome of reactions are important tools to accelerate chemical research. The nature of a reaction is well-described by its name and class, where a reaction class is defined by the general reaction-type and the participating chemical entities. Automating the classification of reactions provides a tool for chemists to search databases and to quickly evaluate and optimise a novel reaction based on the nature of similar reactions. An important outcome of a chemical reaction is its yield, the percentage of successfully converted reactants into the desired product. Computational methods for predicting such yields are highly valuable in synthesis-planning, where high yields are of paramount importance-especially in multi-step reactions. Earlier work used physics-based descriptors or structure-based molecular fingerprints to classify chemical reactions or predict reaction yields. However, computational complexity and inherent biases have introduced seemingly insurmountable challenges to these approaches. Recently, with the availability of large data sets and the resurgence of artificial neural networks (ANN), deep learning-based learned fingerprints have been introduced as an alternative to earlier methods, outperforming them by considerable margins. However, these approaches come with several drawbacks as well. Training a learned fingerprint requires large amounts of data of acceptable quality and must be retrained when new data becomes available, posing a challenge to accessibility and reproducibility. Due to the nature of the ANNs, learned fingerprints are challenging to interpret, as they, for example, require a careful analysis of attention weights. Finally, the training and evaluation of the models require specialised hard-and software to become computationally tractable.