Next, the performance of our optimized 3DDPDs in PCM regression and classification tasks was tested using both random and temporal validation splits (Figure ). The performance of our models was in line with other PCM models trained in similar conditions for subfamilies of GPCRs . In our set, 3DDPDs performed similarly to non-dynamic protein descriptors in classification tasks and regression tasks with random split. These results suggest that the performance of these models had already reached its peak and small differences in the way to represent the protein space did not make a difference. Nevertheless, the best performing models in classification tasks did not reach a high MCC. Models reached a 0.646 ± 0.009 in the random split (Zscale van Westen), and 0.278 ± 0.005 in the temporal split (Zscale Hellberg), hence questioning the relevance of this dataset for such task. Interestingly, protein embeddings (UniRep) showed lower performance across the board, which has also been shown in other datasets compared to sequence-and 3D-based protein descriptors . In the regression task with temporal split, however, 3DDPDs significantly outperformed nondynamic descriptors. Given the more challenging form of validation introduced by the temporal split, the 3DDPDs represent an advantage. These results are likely also the result of performing 3DDPD optimization using this particular task. Nevertheless, similar behaviors have been observed in other benchmarks when using temporal splits compared to random splits . Moreover, in our PCM benchmark ps3DDPDs performed better than rs3DDPDs overall. One reason for this could be the difference in descriptor length: for the GPCR wild type set, rs3DDPDs contained 3,785 features and ps3DDPDs 30 features.