In order to test the complementarity of the 3DDPDs with other protein descriptors, a set of regression models was trained with temporal splits with pairs of dynamic and non-dynamic protein descriptors (Figure ). In all cases, the addition of a 3DDPD on top of a non-dynamic descriptor resulted in similar performance to the models trained exclusively using nondynamic descriptors, or even slightly worse in the case of PhysChem + rs3DDPD. Moreover, the combination yielded statistically worse performance than using the dynamic descriptors alone, particularly in the case of ps3DDPD. This non-complementarity was further confirmed for ps3DDPDs by their exclusion from the most important features for the combination models (e.g. ps3DDPD + PhysChem, Figure ), where only non-dynamic protein descriptor features and ECFP6 compound fingerprint bits were picked up as the top 25 most important for the model. For rs3DDPDs, however, there seemed to be a certain complementarity as both dynamic and non-dynamic protein descriptor features showed up among the top 25 most important for the model (e.g. rs3DDPD + Zscale van Westen, Figure ), even if this did not translate into an improvement in model performance.