  The conformational landscape of proteins is crucial to understanding their
functionality in complex biological processes. Traditional physics-based
computational methods, such as molecular dynamics (MD) simulations, suffer from
rare event sampling and long equilibration time problems, hindering their
applications in general protein systems. Recently, deep generative modeling
techniques, especially diffusion models, have been employed to generate novel
protein conformations. However, existing score-based diffusion methods cannot
properly incorporate important physical prior knowledge to guide the generation
process, causing large deviations in the sampled protein conformations from the
equilibrium distribution. In this paper, to overcome these limitations, we
propose a force-guided SE(3) diffusion model, ConfDiff, for protein
conformation generation. By incorporating a force-guided network with a mixture
of data-based score models, ConfDiff can can generate protein conformations
with rich diversity while preserving high fidelity. Experiments on a variety of
protein conformation prediction tasks, including 12 fast-folding proteins and
the Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our method
surpasses the state-of-the-art method.
