Yusong Wang

Protein conformational ensemble generation has treated coordinated motion between residues as implicit in the learned distribution, which limits direct control. CoMoFlow builds protein-specific collective modes from the equilibrium structure, learns the distribution of their coefficients across the molecular dynamics ensemble, and uses sampled collective displacements to guide SE(3) flow matching through feature conditioning and prior recentering. On ATLAS and de novo proteins it raises DCCM correlation over BBFlow from 0.87/0.83 to 0.93/0.89 and lowers PCA distance from 1.33/0.67 to 1.21/0.59, while allowing generated conformations to be steered along a chosen mode in direction and magnitude.

Protein conformational ensemble generation has treated coordinated motion between residues as implicit in the learned distribution, which limits direct control. CoMoFlow builds protein-specific collective modes from the equilibrium structure, learns the distribution of their coefficients across the molecular dynamics ensemble, and uses sampled collective displacements to guide SE(3) flow matching through feature conditioning and prior recentering. On ATLAS and de novo proteins it raises DCCM correlation over BBFlow from 0.87/0.83 to 0.93/0.89 and lowers PCA distance from 1.33/0.67 to 1.21/0.59, while allowing generated conformations to be steered along a chosen mode in direction and magnitude.