We propose a novel neural network-based approach to modeling protein dynamics using an implicit representation of a protein's surface in 3D and time.

Deep network models are often purely inductive during both training and inference on unseen data. When these models are used for prediction, but they may fail to capture important semantic information and implicit dependencies within datasets. Recent advancements have shown that combining multiple modalities in large-scale vision and language settings can improve understanding and generalization performance.

TheCSGM framework(Bora-Jalal-Price-Dimakis'17) hasshownthatdeepgenerative priors can be powerful tools for solving inverse problems.