EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
This work addresses the problem of accelerating protein dynamics simulation for researchers in computational biology, offering a novel method that improves upon existing deep learning approaches.
The paper tackles the computational challenge of simulating protein dynamics by introducing EquiJump, an SO(3)-equivariant model that bridges all-atom protein dynamics simulation time steps directly, achieving state-of-the-art results on fast folding proteins with a transferable model.
Mapping the conformational dynamics of proteins is crucial for elucidating their functional mechanisms. While Molecular Dynamics (MD) simulation enables detailed time evolution of protein motion, its computational toll hinders its use in practice. To address this challenge, multiple deep learning models for reproducing and accelerating MD have been proposed drawing on transport-based generative methods. However, existing work focuses on generation through transport of samples from prior distributions, that can often be distant from the data manifold. The recently proposed framework of stochastic interpolants, instead, enables transport between arbitrary distribution endpoints. Building upon this work, we introduce EquiJump, a transferable SO(3)-equivariant model that bridges all-atom protein dynamics simulation time steps directly. Our approach unifies diverse sampling methods and is benchmarked against existing models on trajectory data of fast folding proteins. EquiJump achieves state-of-the-art results on dynamics simulation with a transferable model on all of the fast folding proteins.