In order to evaluate the error associated with predicted descriptors on downstream applications, we retrospectively analyzed a published statistical model for the rate of amide coupling reactions with CDI, which relies on DFT-level acid and primary amine descriptors. Furthermore, this case study should elucidate the impact of using predicted descriptors prospective studies where DFT-level descriptors are not available for every compound. We opted to use exclusively predicted descriptors rather than a mix of DFT-derived and predicted descriptors, to allow the model, in principle, to learn to cope with systematic errors in the predicted descriptors. 69 Figure shows the previously published model rebuilt using the analogous 3D GNN predicted descriptors from the models published herein. The model remains statistically similar and directly comparable to that in the original report. To further validate this approach, a virtual screening campaign was used to directly compare the use of models using DFTderived or 3D GNN predicted descriptors. Comparing the two model equations trained on the entire dataset, similarities in the sign and magnitude of coefficients in the equations fit with DFT-derived and 3D GNN predicted descriptors highlight that both models lead to the same mechanistic conclusions regardless of the descriptor acquisition method used. For virtual screening, a total of 2362 amide couplings were generated by pairing acids and primary amines from the test and external sets. Comparisons of rate predictions using exclusively DFT-derived or exclusively 3D GNN predicted descriptors revealed good overall agreement (R 2 = 0.849, MAE = 0.719, Figure ). At the extrema, error in predictions increases, but rate constants are either too fast to measure or slow to the point of no observable reaction in a reasonable timespan, making the predicted outcome functionally the same as no reaction or instantaneous, respectively. However, rate predictions within a realistic, experimentally measurable range of k (-3.5 < ln(k) < 2.5, 3 days to 5 min) exhibit less error (MAE = 0.535). Couplings with acetic acid were the only notably poor predictions and were excluded from the analysis. Ultimately, we conclude that descriptors predicted using 3D GNNs are suitable for statistical modeling applications and valuable for conducting expansive virtual screens.