These results make us confident to propose the use of rs3DDPDs as in mutant descriptors in machine learning tasks. Other works have highlighted the use of dynamic information to predict differences between mutants, such as by extracting normal modes , or time series of changing geometrical features . However, as the changes in protein dynamics did not fully match the in vitro effects from the limited mutagenesis data available, the value in mutant bioactivity prediction needs to be further validated. Mutant clusters generated based on ps3DDPDs captured the most different dynamic changes between variants (Figure ), but this did not result in the expected clustering. The biggest differences in RMSF between mutants were observed in the N-and C-terminus, which are the most flexible regions of the GPCR together with the loops. While the termini have a function in the receptor, in the context of ps3DDPDs it seems to be blown out of proportion. An alternative would be to compute ps3DDPDs for particular regions of interest. For instance, we suggest to analyze functionally relevant residues derived from rs3DDPD feature importance, from observations in the RMSF analysis, or from the literature (for example for cancer-related mutans ).