Over all pharmacokinetic endpoints, SR-GINE performs significantly better than the control GINE (paired t-test, p = 1.98 • 10 -5 ), which uses the standard mean pooling instead of a set layer. Furthermore, the SR-GINE model ). As the benchmarks found in MoleculeNet remain the most used and cited when benchmarking new graph neural network architectures, our results may show a need to adopt a different practice or update MoleculeNet with more recent data sets when assessing possible advantages of graph representation-based approaches. In addition, the difference in performance between graph-and set-based methods with that of the chemical large language model (MolFormer) is not as stark on the Biogen ADME data set as it was in the MoleculeNet benchmarks. Indeed, MolFormer does not perform significantly better than SR-GINE (paired t-test, p = 0.158).