where V r represents the set of n virtual particles at iteration r, and f pm is an SE(3)-equivariant model that takes 3D coordinates as input, corresponding to our 3D pretraining backbone. The term θ pm denotes the learnable parameters. The variables x r i ∈ R t and y r i ∈ R 3 represent the atomic type and coordinates, respectively, where t is the number of atomic types. The number of iterations in the particle movement stage is denoted as R 1 . Since V R1 generated in the PM stage is an approximation of atomic distributions rather than molecular distribution, a Molecule Extraction (ME) step is required to refine VPs into meaningful molecular representations. It employs the pretraining backbone h me to filter and aggregate n virtual particles into m atoms, formulated as: