To estimate the impact of time series descriptors on affinity prediction we compared the overall performance of two types of models; trained with crystallographic descriptors, and with MD derived ts_descriptors, with respect to different target and ligand data splits. Here all analyses refer to RF models trained on 20ns molecular dynamics run. The results of these experiments are summarized on Figure . For randomly split data both models achieve comparable results with respect to PCC (0.73 vs 0.73), r 2 (0.53 vs 0.51) and SD (0.06 vs 0.06). However, in the case of a more challenging target split, an advantage of the model trained on time series descriptors can be seen (PCC mean: 0.6 vs 0.58, median 0.6 vs 0.53), along with a smaller SD (0.07 vs 0.10). This slight advantage can also be seen with respect to the Bemis-Murcko scaffold split. Both target and scaffold splits are more challenging tests as they try to minimize data leakage events. For scaffold split, the MD-based model also achieved better results (PCC 0.63 VS 0.60, r2 0.35 vs 0.29). Taken together, the performance gain and the lower SD of models trained on time series descriptors would suggest better generalizability potential, also with respect to uncharted chemical space. Next we compared each crystallographic descriptor with its time series counterpart to assess their correlations with affinity. Figure shows the results obtained for the 6 different groups of descriptors used. In the group of interaction descriptors, all time series descriptors have increased correlations compared to their crystallographic counterparts (with the exception of the Anionic term). For both types of descriptors, the hydrophobic and vdW interactions show the highest affinity correlation, importantly the time series correlation at least doubles compared to crystallographic descriptors. In the case of a single descriptor a correlation around 0.5 PCC would be considered fairly high. The rest of the interaction descriptor time series are higher than crystallographic, nevertheless overall their correlation values are low for both types of data.