The bioactivity dataset compiled for bioactivity modelling contained 38,701 bioactivity datapoints heterogeneously distributed across the 26 targets (Table in Additional file 1, which contains all supplementary figures). Firstly, the need for PCM modelling in such a set was assessed by comparing the performance of the PCM models to the average performance of individual QSAR models for each of the GPCRs in the set. In all of the modelling scenarios, the worst performing PCM model outperformed significantly the QSAR models: In PCM, models using 3DDPDs performed similarly to using other protein descriptors in classification tasks regardless of the split type (Figure ). One exception was the temporal split classification task, here rs3DDPDs produced slightly worse performance than models using Zscale Hellberg, Stscale, and MS-WHIM (MCC 0.273 ± 0.003 vs. 0.273 ± 0.005, 0.278 ± 0.005 and 0.277 ± 0.004, respectively, Figure ). In the regression task with random split, models using 3DDPDs performed again similarly to models using other protein descriptors (Figure ), with the exception of rs3DDPDs performing slightly but significantly worse than Zscale van Westen (Pearson r 0.832 ± 0.004 vs. 0.836 ± 0.004, respectively) and ps3DDPDs performing slightly better than the Unirep protein embedding (Pearson r 0.835 ± 0.003 vs. 0.832 ± 0.003, respectively). In the regression task with temporal split, however, both types of 3DDPDs outperformed the rest of descriptors (Figure ). The performance of models trained with non-dynamic protein descriptors measured as Pearson r ranged from 0.410 ± 0.003 (Zscale Hellberg) to 0.415 ± 0.004 (PhysChem) passing by 0.410 ± 0.006 (Zscale van Westen), 0.410 ± 0.004 (MS-WHIM), 0.411 ± 0.004 (UniRep), and 0.413 ± 0.005 (Stscale).