In chemistry, electrophilic and nucleophilic reactions are utilized in the design of new protein reactive drugs, identification of toxic compounds, and the exclusion of reactive compounds from high throughput screening. In particular, covalent drugs comprise a class of protein reactive compounds that have seen a lot of interest due to their potential advantages such as better selectivity, longer effective dose, and overcoming drug resistances. Despite that, there are currently no reliable screening tools that go beyond basic substructure matching. In this work, we demonstrate that graph neural networks models are capable of predicting covalent reactivity and capturing chemical motifs by looking at gradient activation heatmaps and how they correlate with chemical theory. We also propose a new dataset, ProteinReactiveDB, which was used to train graph-based models in this work.