We utilize a specialized 3D pretraining backbone tailored for OOMs to build a regression model. This model learns generated molecular conformations and their properties through DFT calculations. Due to the high cost of DFT calculations, only a subset of samples undergo DFT calculations to obtain their properties. Uni-Mol's generalization capabilities enable broader applicability with minimal data. The model is then trained using these conformations as inputs and their DFT properties as labels, optimizing with a smooth L1 loss function: