The findings highlight the inherent challenges and limitations in predicting molecular trajectories. Models like the simple feed-forward network struggled with the dynamic nature of the data, whereas time-sensitive models like RNNs showed improved performance. The E3NN and GCN, in particular, illuminated the effectiveness of geometrically consistent and graph-based approaches, respectively. However, the diffusion models, while novel in their approach, faced difficulties in capturing physically plausible and complex molecular dynamics. This work underscores a crucial point: the trajectory of aspirin changes stochastically from frame to frame, presenting a near-random process that models must learn to predict. This randomness poses a significant challenge in achieving meaningful learning and accurate predictions. Despite the preliminary nature of this work, it reveals the potential of machine learning, particularly graphical methods, in representing molecules and paves the way for more sophisticated and accurate models that promise advancements in the fields of computational chemistry and drug discovery.