Herein, we present an expanded and updated GNN model for BDE prediction. We also consider BDFE (bond dissociation free energy) values, the standard free energy change associated with the dissociation. The inclusion of S, Cl, F, P, Br, and I was motivated by the frequency of these elements alongside C, H, N, and O in approved drugs and enables ML-based BDE predictions to be applied routinely to medicinally and pharmaceutically relevant molecules. We describe the development of a large dataset (BDE-db2) containing over 530,000 unique M06-2X/def2-TZVP computed BDE and BDFE values, which underpins this effort. We explore the performance of the model on focused datasets of C(sp 2 ) and C(sp 3 ) halogenated molecules relevant to medicinal (polyhalogenated building blocks), atmospheric (halocarbons), and environmental chemistry (perand polyfluoroalkyl substances, PFAS). Improvements in predictive performance are obtained by analyzing the latent space covered by these datasets and by the addition of a small number of new training samples. The expanded model retains the same levels of accuracy for C, H, N, and O as in previous work while significantly expanding the applicability to new bond types, which are predicted with similarly high levels of accuracy.