The rs3DDPD was designed to capture the differences across different parts of the target. The second type, protein specific (ps)3DDPD, is closer to whole sequence protein embeddings and was designed to capture the differences between targets in a set. Consequently, atomic data was aggregated per target for rs3DDPDs and for all targets for ps3DDPDs and its dimensionality was reduced via principal component analysis (PCA). A number of principal components (PCs) for each atom were selected and, in the case of rs3DDPDs, grouped per residue. A second dimensionality reduction step was applied on residue data and the selected PCs were placed in their matching sections corresponding to a multiple sequence alignment (MSA) of the targets of interest. For ps3DDPDs, the PCs selected per atom were grouped per target, resulting in the final descriptor. The 3DDPD generation strategy described above was optimized by comparing the descriptors' performance on PCM modelling tasks. GPCRs were selected as the protein family for this case study given the availability of a large number of MD trajectories freely in the GPCRmd database . Particularly, the focus laid on Class A GPCR apo structures in the inactive or intermediate conformations, more broadly represented at the time of the analysis. The PCM dataset contained 26 GPCRs with available MD trajectories in GPCRmd and high quality data in the Papyrus bioactivity dataset , in total 38,701 datapoints.