We compared the performance of end-to-end learned representations, as in our GNN model, against cheminformatics-based features such as circular atomic fingerprints. We also evaluated whether our GNN could be improved by including additional features, such as DFToptimized bond lengths (Fig. ). We studied these models' learning by systematically increasing the number of training samples: while the number of samples was randomly selected for three different runs, they were kept consistent across the different models. For GNN Model Four natural products containing F, Cl, Br, and I were identified (Fig. ): Nucleocidin, Salinosporamide A, Tyrian purple, and Levothyroxine. All of these natural products were fragmented and optimized with a similar method as the training database. molecule have an R 2 equal to or higher than 0.9 and a mean absolute error under 2 kcal mol -1 (Fig. ).