Motivation. In this investigation, I aim to address the fundamental computational and biochemical question: how can we accurately predict molecular trajectories? This study is inspired by recent approaches that tackle the ligand docking problem, many which search for molecular conformational poses through generative diffusion models . Meanwhile, I venture into a more foundational domain by delving into the underlying principles of molecular behavior. I propose a machine learning-based framework for predicting the dynamic evolution of molecular conformations. This method marks a significant departure from the traditional, computationally demanding techniques like molecular dynamics that dominate the field. I integrate recent advances in molecular modeling. The GeoDiff paper, for instance, illustrates the effectiveness of using atomic coordinates to create new molecular conformations within a graph-based representation, where atoms and bonds are depicted as nodes and edges . Moreover, the concept of equivariance in diffusion models, highlighted in pioneering studies such as OADiffReact by Duan et al. and E3 diffusion by Hoogeboom et al., largely motivate the methodology of this work . These models ensure the physical coherence of generated molecular poses, adhering to rototranslational and permutation invariance, which are essential for precise molecular depiction.