Of the 5 approaches tested for RT prediction on the Enamine dataset, the two best performers, Chemprop and GATv2Conv+DL, gave very similar estimation metrics at the 95% confidence level. The mean cross-validation error in both cases was around 2.4% of the experimental scale, which was close to the METLIN SMRT model errors. We therefore selected GATv2Conv+DL, implemented by PyTorch Geometric, to build and estimate the final model for our in-house production purposes.