An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 7 references that are in the map. First posted on arXiv 23 Nov 2022.
Builds on
5 of 7 references.
References · 5
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
- 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
- 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 343
- 2022One Transformer Can Understand Both 2D & 3D Molecular Datacited by 77
Built on by
Citing papers per year: 2023: 1.
1 paper.