During pretraining, Uni-Mol focuses on two core tasks: 3D position reconstruction and masked atom prediction, both aimed at capturing the deep structural characteristics of OOMs. In the 3D position reconstruction task, the model learns molecular geometry by correcting perturbed atomic coordinates. Specifically, it recovers the original unperturbed 3D coordinates from randomly distorted inputs. To encode 3D positional information, Uni-Mol constructs spatially invariant position encodings based on the Euclidean distances between atomic pairs. These encodings facilitate the interaction between atomic and pairwise representations within the multi-head self-attention mechanism. Following the formulation in Uni-Mol, for any given atom pair (i, j), the update at layer (l + 1) is defined as: