Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-art approaches are typically restricted to generating proteins with only static target properties, such as motifs and symmetries. In my work, I aim to develop novel generative modeling frameworks capable of performing protein structure generation conditioned on dynamic structural properties. Structural dynamics plays a key role in protein functionalities, such as catalysis, molecular recognition and ligand binding, thus we hope that our methods will advance molecular design for biologically relevant functionalities.