Proteins are the workhorses of the cell, powering the biological processes that make life possible. Their precise choreographies catalyze reactions, transport molecules, and build cellular structures, and modeling these intricate dances is crucial to understanding and manipulating biological systems.
While Molecular Dynamics (MD) simulation offers a powerful tool to study these movements, its computational demands and latency have limited its widespread use. Instead, learning from trajectory data, generative AI has emerged as a powerful surrogate for overcoming these limitations and providing faster insights into protein dynamics.
In collaboration with NVIDIA, the MIT Atomic Architects and SISSA, we recently introduced a new atomistic deep learning model – EquiJump – for learning and accelerating protein dynamics simulation through large jumps in simulation time. EquiJump leverages Euclidean equivariant neural networks within the generative framework of Stochastic Interpolants for directly transporting 3D all-atom proteins between simulation time steps.