To systematically investigate the structural properties of OOMs and efficiently explore their extensive chemical space, we have utilized Uni-Mol for 3D pretraining. Uni-Mol is a universal 3D framework for molecular representation learning that effectively learns and outputs 3D positional information through pretraining with a large volume of unlabeled data. The model takes atom types and atomic coordinates as inputs, where atomic representations are initialized via an embedding layer. Pairwise representations are derived from invariant spatial positional encoding based on pairwise Euclidean distances, ensuring global rotational and translational invariance (SE(3)-invariance). Within the model, these representations interact through the self-attention mechanism of the Transformer , allowing for the capture of both local and global molecular features.