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

2022/11/23 by Yusong Wang, Shaoning Li, Wang, Yusong +11
Chemistry · Computer Science · Materials Science · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Various Chemistry Research Topics

paper · pdf · doi:10.48550/arxiv.2211.12791

openalex publication_date 2022/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Related

• On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
• Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
• Heterogenous Ensemble of Models for Molecular Property Prediction
• ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules
• GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction