Beyond applications relating directly to carboxylic acids and amines, it is worth noting that many of these carboxylic acid descriptors are expected to be directly correlated to descriptors for functional groups made from similar feedstocks (e.g., aldehydes, esters, boronic acids, and sulfonic acids) that are commonly used in synthetic campaigns. Much like Hammett parameters-experimentally determined values for a range of carboxylic acids-we envision that our descriptors would be similarly broadly applicable beyond the specific chemistry of carboxylic acids. Ultimately, this study provides a blueprint not only for descriptor library building but also for expanding those libraries to myriad related compounds using 2D and 3D GNN models. Taken together, these advances represent a step towards the overall effort to reduce the computational cost and lower the barrier of entry to perform datadriven chemical campaigns. (69) In practice, we found there was no particular advantage to using 3D-trained models over DFT-trained models when predicting amide coupling rates with 3D descriptors (SI section 6.3).