论文标题
在OGB大规模挑战 @ Neurips 2022中的Visnet，Transformer-M和预训练模型的合奏
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
论文作者
论文摘要
在技术报告中，我们为OGB-LSC 2022图回归任务提供了解决方案。该任务的目标是预测PCQM4MV2数据集上给定分子的量子化学特性，同性恋差距。在竞争中，我们设计了两种模型：变压器-M-VISNET，它是一个几何增强的图形神经网络，用于完全连接的分子图和预审预测的3D-VISNET，它是通过从优化结构中蒸馏出的地理信息，是一种预处理的Visnet。通过22个型号的合奏，Visnet团队在测试范围内实现了0.0723 eV的MAE，与去年竞争中最佳方法相比，误差大幅下降了39.75％。
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.
