  We present a systematic investigation using graph neural networks (GNNs) to
model organic chemical reactions. To do so, we prepared a dataset collection of
four ubiquitous reactions from the organic chemistry literature. We evaluate
seven different GNN architectures for classification tasks pertaining to the
identification of experimental reagents and conditions. We find that models are
able to identify specific graph features that affect reaction conditions and
lead to accurate predictions. The results herein show great promise in
advancing molecular machine learning.
