One of the main limitations of our current approach is the reliability of MD simulations as input data for the computation of 3DDPDs. Firstly, the issue of MD stochastic stability is not addressed here , as different replicates are not used to compute our 3DDPDs. In the future, an analysis of the impact of additional replicates in the data collection phase should be conducted. The introduction of replicas could be done twofold, either by directly using the average of the atomic coordinates as starting point, or by using a bigger stack of individual atomic coordinates in the first PCA. Secondly, MD simulations are computationally expensive to generate, which can be a bottleneck. Similar publicly available repositories to those existing for GPCRs (i.e. GPCRmd) would help increase the applicability domain of dynamic descriptors to other protein families in the future. Finally, by extracting features from the MD trajectory, there is a constant need to make informed decisions to leave out data and reduce the amount of information available. Recently, graph neural networks (GNNs) have been used to represent MD trajectories . The network embeddings could be used as dynamic descriptors instead, letting the machine decide which features are more relevant, although such approaches do not necessarily produce better results . As a last note on applicability, in our current work the description of the dynamic behavior of a protein is tackled, but the conformational changes introduced by ligand binding are not taken into account. Running MD simulations for every complex in the dataset would not be advisable, but the dynamic binding space could be represented for example by an additional term describing dynamic pharmacophores .