An interesting result was achieved by the models trained only with ECFP4 and ligand properties. These models, having no information about the target, show elevated performance in affinity prediction, suggesting they mostly learn biases and random relationships in the data rather than predict affinity as a function of both target and ligand complex. Similar conclusions in the context of protein-ligand affinity predictions have been noted in other work as well . SHAP analysis presents the 20 most important features, together with their utilization in the form of counts, over all 20 models of each type; static-and time series based (Table ). Each model is using a slightly different set of descriptors (or ts_descriptors) depending on data splits it was trained on. SHAP analysis of a single static-and time series based model is presented on Figure in the supplementary materials as reference. One striking difference between the static and time series models is the heavy reliance on ligand descriptors by the former. Out of 20 most influential descriptors, nearly half of them (9/20, 45%) are ligand based. The same comparison with time series models shows they rely only on 25% (5/20) of ligand based ts_descriptors. Even more, with static models 5/9 of the most influential ligand descriptors are simple 2D physicochemical features. With time series models just 1/5. These results may explain the poorer performance of static models observed with the scaffold splits. SHAP shows the MD-derived models rely mostly on a different set of features compared to models trained on static representations. There are three motion descriptors important for the time series models. The pocket contacts (both old and new) and ligand RMSD are especially interesting as they are exclusive to the MD-derived representation. Taken together, the SHAP results show that the time series models use different sets of features, rely on simulation exclusive motion descriptors, and use less simple ligand features, rendering them possibly less prone to small molecule bias present in datasets.