Edge-featured Graph Attention Networks (EGAT) are a subset of Graph Attention Networks (GAT). The basic idea behind EGATs is to use the features of each edge to create an attention score that is used to weight the information mixing between nodes. Each pass through an EGAT layer results in the mixing of information between nodes as determined by learnable attention scores. Thus, the use of n EGAT layers results in the mixing of information from nodes up to n edges away into the fingerprint of each node. A fingerprint of the whole reaction graph is obtained by pooling the node and edge fingerprints, which can be used for reaction property prediction.