Surprisingly, motion descriptors which we thought would substantially contribute to the affinity prediction performance show only minor gains compared to static, crystallographic descriptors. Only two of them (pocket_contact_new and pocket contacts_old) have PCC around 0.5 or above. Results suggest that pocket internal contacts (both preserved and new) are more correlated to affinity than e.g. pocket-ligand interactions and changes in RMSD of both molecular entities. Taken together, although individual performance of the ts_descriptors is favorable, it does not seem to add up to the final model performance (Figure ). This effect is probably due to non-linear cross-correlations present between ts_descriptors. This effect is further enhanced by the noise in the data. Common correlation methods such as Pearson's, Spearman's or Kendall are able to find linear and monotonic relationships between variables, assuming little noise. Other methods measuring potential non-linear correlations test only the independence of variables without quantifying the strength of the relationship, and are also very sensitive to noise and outliers. Therefore such nonlinearity is currently difficult to filter beforehand .