SE(3)-Stochastic Flow Matching for Protein Backbone Generation
arxiv(2023)
摘要
The computational design of novel protein structures has the potential to
impact numerous scientific disciplines greatly. Toward this goal, we introduce
FoldFlow, a series of novel generative models of increasing modeling power
based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions -- i.e.
the group $\text{SE}(3)$ -- enabling accurate modeling of protein backbones. We
first introduce FoldFlow-Base, a simulation-free approach to learning
deterministic continuous-time dynamics and matching invariant target
distributions on $\text{SE}(3)$. We next accelerate training by incorporating
Riemannian optimal transport to create FoldFlow-OT, leading to the construction
of both more simple and stable flows. Finally, we design FoldFlow-SFM, coupling
both Riemannian OT and simulation-free training to learn stochastic
continuous-time dynamics over $\text{SE}(3). Our family of FoldFlow, generative
models offers several key advantages over previous approaches to the generative
modeling of proteins: they are more stable and faster to train than
diffusion-based approaches, and our models enjoy the ability to map any
invariant source distribution to any invariant target distribution over
$\text{SE}(3)$. Empirically, we validate FoldFlow, on protein backbone
generation of up to $300$ amino acids leading to high-quality designable,
diverse, and novel samples.
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