One of the important conclusions derived from a large number of diverse MD simulations is that conducting more lengthy simulations does not improve the performance of predictive models (Figure ). What is more, multiple time series descriptors (ts_descriptors) derived from 20ns simulations obtained significantly higher Pearson correlation values compared to 200ns (Figure ). One possible explanation is that longer simulations introduce more variational noise, which is difficult for simple ML models to filter. When testing narrow time windows (up to 0.2ps), a short simulation was enough to extract relevant features. This conclusion brings hope to the inclusion of MD simulation into protocols concerning diverse chemical library screening and hit prioritization, and is also consistent with some previous works done on single targets . When comparing models trained on static (based on crystallographic data) and dynamic (MD derived) descriptors, the two models consider different descriptors to be most relevant. Interestingly, we noted a number of time series derived descriptores with significantly better correlations compared to their static counterparts (Figure ). Their summarized influence however did transfer only slightly to improved affinity prediction performance. Given that we performed cross-correlation filtering, this would suggest that non-linear correlations may decrease the overall performance. Ablation analysis and SHAP studies (see supplementary materials S3) further confirm these findings, showing the two models employ different types of descriptors. In the case of more challenging target splits, an advantage of MD-derived models can also be observed, highlighting potential generalizability advantages of the time series descriptors. In summary, we have developed the largest, publicly available dataset of molecular dynamics simulations of protein ligand complexes simulations. We treated the MD-derived data as time series, in order to extract meaningful features that could improve the task of affinity prediction. We found that using simple ML models in combination with a relatively small number of descriptors yields results comparable to highly complex models based on neural networks, with a large number of parameters. We highlight that short (~20ns) molecular dynamics simulations provide relevant information for affinity prediction and that elongation of the simulations does not improve the predictive power. Finally, we conclude that models based on MD-derived data treated as time series do not achieve significantly better results compared to models based on crystallographic data, although they appear to generalize better. It should be emphasized that the analyzed set, despite being the largest in the currently available literature, is still relatively small and the conclusions should be tested against a larger body of structures.