Computer-aided drug discovery (CADD) techniques have made an impact on the pharmaceutical industry by enhancing the efficiency of the drug development process, reducing time, cost, and labor . Despite these advancements, accurate prediction of binding affinity continues to pose a considerable challenge, often bottlenecked by the inherent complexities of molecular interactions . Progress in machine learning (ML) and deep learning (DL) has shown promise in overcoming some of these hurdles , revealing in large datasets intricate, non-linear properties and relationships in protein-ligand complexes. Current state-of-the-art methods achieve Pearson correlation coefficient (R p ) around 0.7-0.85 on the CASF2016 benchmark , which is a significant improvement compared to the classical scoring functions. Despite this achievement, challenges remain, particularly with the generalizability of these models. While definitely useful, traditional static computational approaches like molecular docking only provide a snapshot view of a molecular complex, without its temporal dynamics . Molecular dynamics (MD) simulations introduce a vital temporal dimension to protein-ligand complex studies. Such simulations allow for more detailed observations of how drug molecules interact with biological targets over time . Over the last years integration of MD simulations with ML and DL has been applied with varying success in different drug discovery tasks and specific campaigns. In the case of affinity prediction, Ash and Fourches in 2017 analyzed 87 ERK2-docked ligand complexes by computing chemical descriptors derived from 20ns molecular dynamics (MD) trajectories. They showed that models trained on MD derived descriptors were able to distinguish the most active ERK2 inhibitors from the moderate/weak actives and inactives. They claimed that the descriptors extracted from MD trajectories are highly informative and, having little correlation with classical 2D/3D descriptors, could augment chemical libraries screening tasks, candidate design and lead prioritization. A similar concussion was presented by , who performed molecular docking of 43 compounds associated with Caspase-8, with consecutive 10-ns MD simulations of top scoring complex for each ligand. They investigated 770 2D and 115 3D descriptors together with 4 descriptors extracted from MD simulations: solvent accessible surface area (SASA), radius of gyration (Rg), potential energy and total energy, in the form of mean and standard deviation (8 descriptors in total). They reported that ML models trained on MD data had the most balanced accuracies and AUC values, compared to the 2D and 3D descriptor models, and that models using a combination of 3D and MD descriptors had the best performance. A counter experiment was performed by using the BCR-ABL tyrosine-kinase and 15ns MD simulations of Imatinib and a large series of its derivatives. In conclusion the authors stated that incorporating MD based matrices could not improve the binding affinity prediction ability of either deep NN nor random forest (RF) QSAR models. However, their models did show reduced prediction error, indicating the negative effect of simulation noise becoming stronger as the number of snapshots increased. An approach to compare different ML models trained on descriptors obtained from MD trajectories was presented by . Using three different targets and a maximum of 433 complexes predicted by docking per target, the results for MD augmented approaches were greatly dependent by target. The paper concludes the use of MD does not generally improve screening results and may only be justified in certain cases. Given that the models were trained with descriptors generated from every frame, this may have been a challenge for simple ML models due to the large number of frames and small number of examples. In addition, the low MM/PBSA and Glide scores suggest that the analyzed collections were rather difficult. In conclusion, current research indicates the complexity of leveraging molecular dynamics (MD) data, suggesting it is target specific and can depend on the noise to signal ratio, e.g. number of frames, the length of MD simulation, etc. It is difficult however to draw definite conclusions as only a handful of targets have been tested so far. In this study we have generated the largest set of MD simulations to date, encompassing a broad array of protein-ligand complexes. We developed a comprehensive set of descriptors that utilize various aspects of MD-derived data, and implemented a rigorous feature selection mechanism to tailor the number of features to the analytical methods employed. By training ML models on MD-derived time series descriptors from over 800 unique protein-ligand complexes, we seek answers to the following questions: 1) what are the complex specific and simulation specific features influencing models' affinity prediction outcomes? 2) whether time series representations of MD are beneficial and how do they generalize? 3) Can the MD-derived descriptors augment and/or replace current crystallographic derived descriptors? Based on our findings we present general guidelines and assessments on how such an approach influences the in silico affinity prediction on a large scale.