In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In the competition, we designed two kinds of models: Transformer-M-ViSNet which is an geometry-enhanced graph neural network for fully connected molecular graphs and Pretrained-3D-ViSNet which is a pretrained ViSNet by distilling geomeotric information from optimized structures. With an ensemble of 22 models, ViSNet Team achieved the MAE of 0.0723 eV on the test-challenge set, dramatically reducing the error by 39.75% compared with the best method in the last year competition.

翻译：在本技术报告中，我们提出了OGB-LSC 2022图回归任务的解决方案。该任务的目标是基于PCQM4Mv2数据集预测给定分子的量子化学性质——HOMO-LUMO能隙。在竞赛中，我们设计了两种模型：Transformer-M-ViSNet——一种用于全连接分子图的几何增强图神经网络；以及Pretrained-3D-ViSNet——通过从优化结构中蒸馏几何信息预训练的ViSNet模型。通过集成22个模型，ViSNet团队在测试挑战集上实现了0.0723 eV的平均绝对误差，相较于去年竞赛中的最佳方法，误差显著降低了39.75%。