PhiFold: Towards Dynamic Protein Design with Physics-Structured Covariance Modeling

arXiv:2609.32309 · cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Consistency — 10× in this paper · explained in 3 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Fidelity — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Anisotropy — 3× in this paper · explained in 3 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Frustration — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Learning residue level protein dynamics with multiscale Gaussians
• SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins
• SE(3) diffusion model with application to protein backbone generation

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