The use of 3DDPDs as protein descriptors in PCM bioactivity modelling tasks was tested for our GPCR dataset. For this purpose, the performance of random forest (RF) models was benchmarked using 3DDPDs in combination to ECFP6 molecular fingerprints against models using as protein descriptors one of five other one-hot encoded descriptors (i.e. Zscale in two modalities, STscale, MS-WHIM, and PhysChem) or one protein embedding (i.e. Unirep). The benchmark was carried out for classification and regression tasks using two different types of training-test target-stratified splits: 80:20 random split and temporal split with 2013 as cutoff year for the test set. The temporal split was introduced as a more accurate representation of a drug discovery campaign where data from the past is used to predict novel chemical entities developed later in time.