Although two data split strategies (i.e. random and temporal) were applied in both regression and classification PCM tasks, the optimization strategy was driven mostly by the results in the most demanding task, regression with a temporal split. First, the "dynamic" properties derived from atomic coordinates were optimized. Here, the use of mean, median, and standard deviation from the mean (SD) or just the SD, representing the "rigidity" of each atomic coordinate was benchmarked. For rs3DDPDs, using SD resulted in better performance (Figure ), contrary to ps3DDPDs (Figure ). The number of frames included in each trajectory split was also optimized, where 100 or 500 frames yielded similarly better results (Figure ), so 100 frames were selected further. The variance explained by the selected number of PCs on atom data was optimized and set at 95% for both rs3DDPDs and ps3DDPDs (Figure ); and similarly the number of PCs on residue data was optimized and set to 5 not to explode the number of features (Figure ). Furthermore, the inclusion of atomic data from all heavy atoms or non-carbon atoms only was tested. The former option was significantly better for both rs3DDPDs (Figure ) and ps3DDPDs. Finally, residue selection strategies were tested to focus the 3DDPDs on the protein binding site (Figure ). These selections were based on structural-driven MSAs at different protein family levels, starting from the full sequence, then the binding pocket of class A GPCRs, then specific GPCR families, such as nucleotide receptors, then GPCR subfamilies, such as adenosine receptors, and finally, target-specific binding pocket such as the adenosine A1 receptor. To ensure a consistent number of features per descriptor, in rs3DDPDs only the first two options could be tested, where class A binding pocket performed significantly worse than full sequence (Figure ). In ps3DDPDs all selection methods performed similarly except for the family and target pockets that performed significantly worse (Figure ).