To understand the contributions of static and time series descriptors on model behavior we performed ablation studies, including SHAP analysis . Table provides a summary of ablation studies where only a certain group of descriptors or ts_descriptors are used for training. Overall, for the target splits the time series descriptors based models perform better compared to their static models counterpart, however we note the difference is not major. The highest performance was obtained when training exclusively with pocket property ts_descriptors, closely followed by pocket geometry ts_descriptors. These results are in line with Figure where the time series representation of pocket properties provided substantially more information useful for affinity correlation than a static representation. Interestingly, for random splits, models trained only on static pocket property descriptors perform better than models trained on their time series counterparts. The situation changes with target splits; the static models perform significantly worse compared to the MD-derived models, from which they were superior. One explanation of this result is the interdependence of pocket and ligand descriptors. Since pocket descriptors are calculated with respect to the ligand position, ligand information is implicitly contained, even more with a dynamic representation of the pocket. The highest difference between crystal and MD derived models is obtained when training with interaction descriptors (0.450 vs 0.257). This result confirms our initial hypothesis that the interaction and motion descriptors when represented as time series provide novel and useful information in the context of affinity prediction. However these ts_descriptors, when combined with other types fail to make a substantial difference in performance. This might indicate the need for a more thorough representation of motion and interactions present in ligand-receptor complexes, compared to the setup tested in this work.