Equivariant Diffusion

Equivariant diffusion models are a class of generative models designed to create data that respects underlying symmetries, such as rotations and translations, crucial for applications involving 3D structures like molecules and proteins. Current research focuses on developing and applying these models, particularly those leveraging SE(3) or E(3) equivariance, within various domains including drug discovery (de novo ligand generation, structure-based design), materials science (crystal structure prediction), and robotics (trajectory prediction, manipulation planning). This approach offers significant advantages over traditional methods by generating more realistic and physically plausible structures, leading to improved efficiency and accuracy in diverse scientific and engineering applications.