In this work 3D dynamic protein descriptors (3DDPDs) were developed that capture the dynamic fluctuations of GPCRs as observed in MD simulations. Our one-hot encoding (rs3DDPDs) and embedding-like (ps3DDPDs) descriptors matched the performance in PCM tasks of non-dynamic state-of-the-art protein descriptors, outperforming them in regression tasks with a more challenging temporal split validation. Moreover, by mapping the most important rs3DDPD features in regression models to their GPCR generic positions it was showed that 3DDPDs represent biologically relevant information for ligand binding and activation. Finally, 3DDPDs were employed to discriminate mutant GPCRs based on their dynamic behavior with promising results that could be translated to the field of oncological drug discovery.