In the following, the definition of outlier predictions follows that given in (8), i.e., outlier predictions have absolute errors in excess of 0.5 pH units. GCN models employing four layers, i.e., considering four nearest neighbors, produced markedly fewer outliers for the peptide dataset compared to results reported in either ( ) or (8), despite slightly higher root-mean-squared (and mean absolute) errors, when OHE of the AAs was used. No meaningful learning was achieved by GCN models when physical descriptors were used, either alone or in addition to the OHE. In contrast, GIN models employing four layers were able to produce similar results for all three variations of the feature encoding. More computational effort was needed, however, to ensure early convergence was avoided when physical descriptors were used, as compared to the easierto-train OHE models. For this reason, a simple genetic algorithm-based preselector was used for the weights and biases of the neural network. Use of the preselector allowed all runs of the GIN models to consistently converge to similar results, which markedly outperformed previously published results with respect to the number of outlier predictions for the peptide dataset, but not the protein dataset. Ranges of RMSE values and number of outlier predictions obtained from six different runs of the different GIN models are summarized in Table .