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This work proposes BackFlip-2: a fast SE(3)-equivariant graph neural network trained to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure, and shows that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins.
State Space Models can now generate time series with provable universality, outperforming fixed-grid models, especially on irregular data.
