I initiated the aspirin's trajectory in the random walk and Monte Carlo Markov Chain (MCMC) methods from the first frame of the test set. At each step t, the molecule underwent a rototranslation, adhering to a normal distribution. In the MCMC method, I applied the Metropolis-Hastings criterion, assuming no selective pressure for convergence into local energy minima. During the random walk analysis, I observed that the predicted structure of the molecule gradually diverged from its actual trajectory (Figure ). I hypothesized that this divergence could be attributed to an excessively large step size in the updates. Such a step size could lead to the molecule assuming a conformation that deviates too rapidly or slowly from the actual rate of change observed in the aspirin trajectory calculated using the [Psi4, cc-pVDZ] method. Even after experimenting with various update rates, the most effective configuration yielded an RMSD of 0.66 from the training set poses, as illustrated in Figure . The MCMC method resulted in a similar RMSD value. This is likely due to the absence of an external force field in the model, leading to the molecule's unpredictable behavior (Figure ). For comparison, a baseline model that consistently predicts the initial pose throughout the trajectory results in an RMSD of 0.6 (not shown). This demonstrates the limitations of these statistical methods in accurately capturing the dynamic changes in the molecule's structure over time.