Bond dissociation enthalpies and free energies are fundamental quantities used to assess reaction thermodynamics. BDE values also influence reaction kinetics and are often used as essential ingredients to understand mechanism and selectivity. We have developed a broadly applicable BDE prediction tool based on a graph neural network that yields quantitative predictions close to DFT values across a range of organic molecules containing heteroatoms. This tool enables a broader range of chemical space to be studied by this approach than was previously possible, which now includes aromatic and aliphatic compounds with multiple halogen atoms relevant to medicinal and atmospheric applications. For multiply halogenated chlorofluorocarbons, this approach yields results within 2 kcal/mol of experimental BDE values. For training dataset sizes on the order of thousands or tens of thousands of compounds, we observe that the learned embeddings of the GNN are not improved by the addition of additional QM descriptors and that the model learning performance exceeds a more traditional cheminformatics approach using fixed circular