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In the technical report, we provide our solution for OGB-LSC 2022 GraphRegression Task. The target of this task is to predict the quantum chemicalproperty, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In thecompetition, we designed two kinds of models: Transformer-M-ViSNet which is angeometry-enhanced graph neural network for fully connected molecular graphs andPretrained-3D-ViSNet which is a pretrained ViSNet by distilling geomeotricinformation from optimized structures. With an ensemble of 22 models, ViSNetTeam achieved the MAE of 0.0723 eV on the test-challenge set, dramaticallyreducing the error by 39.75% compared with the best method in the last yearcompetition.

An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022