To further validate the descriptor prediction models for applications in medicinal chemistry, we specifically evaluated the external validation acids and amine sets. Example drug molecules adatanserin, bitopertin, and daridorexant, which have all been synthesized via amide coupling, highlight the complexity of compounds included in the external validation set (Figure ). In general, descriptor models exhibited notably poorer performance on these complex fragments compared to our test set (Figure and) as measured by R 2 , however, we found that the MAEs of the test set and external validation set were typically comparable for each descriptor. We hypothesize the poor R 2 values are due in part to the smaller dataset size that results in models that are more sensitive to outliers. Additionally, the relatively narrow range of descriptor values for the external validation set as compared to the rest of the library leads to a flat response surface for many descriptors. Overall, we found the majority of descriptors pre-dicted for molecules in the external validation set were within one MAE (defined as the test MAE from 2D models or 3D models when available). This that the models suitably predict descriptors for drug-like molecules to facilitate their application in medicinal chemistry pursuits.