While published descriptor libraries may attempt to incorporate common substrates or ligands, it is not practical (or possible) to precompute DFT-level descriptors for every compound a user may wish to featurize. For successful dataset design or predictive modeling, the defined descriptor set needs to be calculated for each new compound of interest. Replicating a full descriptor calculation workflow to add even a single compound to the library can require tedious coordination of numerous software packages, license agreements, computing clusters, etc. Automated workflows to perform these tasks can mitigate several of these challenges but do not circumvent the computational cost. Thus, it would enable downstream applications if an existing library could be exploited to predict relevant descriptors for new compounds within seconds Across the chemical sciences, machine learning (ML) models have demonstrated the ability to serve as surrogates for electronic structure calculations and other simulation techniques connecting molecular structures to computed properties. Previously, prediction of DFT-level descriptors has been accomplished for single properties on a broad range of mole-cules. Additionally, ML property prediction has been used to expand a DFT-level descriptor library of monophosphines 200-fold by combinatorializing substructures present in the existing library. Herein, we describe a case study investigating the prediction of a set of conformationally-informed descriptors collected for a single conserved reactive moiety-either a carboxylic acid or a primary/secondary alkyl amine (Figure ). Our selection of carboxylic acids and amines was motivated by the ubiquity of amide couplings in medicinal chemistry, as well as our recent efforts to correlate the reaction rates of amide couplings with DFT-level molecular descriptors of carboxylic acid derivatives and primary alkyl amines. We envisioned a method wherein a user would be able to simply supply a SMILES string (or draw a chemical structure) to obtain high-fidelity predictions of DFT-level descriptors for use in downstream applications without the need for HPC resources (Figure ). To obtain accurate predictions, we needed to address the challenge of predicting diverse descriptors (i.e., steric, electronic, and stereoelectronic properties at the molecule-, atom-, and bond-level) that account for the dynamic range of properties stemming from the conformational flexibility of compounds in these classes (Figure ). We applied graph neural networks (GNNs) trained on expansive libraries to predict these diverse DFT-level descriptors. We demonstrated these predicted descriptors are appropriate for data-driven modeling of medicinally-relevant carboxylic acid and alkyl amines. Moreover, Hammett parameters for aryl carboxylic acids are a cornerstone of physical organic chemistry; therefore, we envision an extensive library of carboxylic acid descriptors should have widespread applications as surrogate descriptors. The rapid prediction of descriptors also greatly expands the direct applicability of the carboxylic acid and alkyl amine descriptor libraries, reducing the barrier of entry to dataset design and predictive modeling.