In this work, we develop a graph neural network (GNN) that can predict atomic transition charges (ATCs) for the atoms in a molecule. A GNN is a machine learning model that makes predictions using a graph embedding technique. A graph G = (V, E) contains a set of nodes V connected together by edges E. For the case of the molecule data used in this work, the nodes represent the atoms in the molecule, while the edges represent the bonds between atoms in the molecule. During the training of the GNN, each atoms 'learns' about its local environment by passing messages about its features between itself and its neighboring atoms. During this process, the GNN identifies trends related to local environments within the molecules, enabling it to predict ATC values.