OGB
PCQM4Mv2
Structure
3,746,619Graphs
14Nodes
15Edges
9Node Feature Dim
3Edge Feature Dim
Coverage
111Results
1Variants
16Papers
- ICLR 2026: Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions ICLR 2026
- AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
- GoMS: Graph of Molecule Substructure Network for Molecule Property Prediction GoMS: Graph of Molecule Substructure Network for Molecule Property Prediction
- When does global attention help: a unified empirical study on atomistic graph learning When does global attention help: a unified empirical study on atomistic graph learning
- ICML 2024: Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers ICML 2024
- NeurIPS 2024: Towards Principled Graph Transformers Towards Principled Graph Transformers NeurIPS 2024
- NeurIPS 2023: Graph Convolutions Enrich the Self-Attention in Transformers! Graph Convolutions Enrich the Self-Attention in Transformers! NeurIPS 2023
- Enhancing Graph Neural Networks with Quantum Computed Encodings Enhancing Graph Neural Networks with Quantum Computed Encodings
- IJCAI 2023: IJCAI--23 Formatting Instructions IJCAI--23 Formatting Instructions IJCAI 2023
- ICLR 2023: Specformer: Spectral Graph Neural Networks Meet Transformers Specformer: Spectral Graph Neural Networks Meet Transformers ICLR 2023
- ICLR 2022: One Transformer Can Understand Both 2D & 3D Molecular Data One Transformer Can Understand Both 2D & 3D Molecular Data ICLR 2022
- GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions
- NeurIPS 2022: Pure Transformers are Powerful Graph Learners Pure Transformers are Powerful Graph Learners NeurIPS 2022
- NeurIPS 2022: Recipe for a General, Powerful, Scalable Graph Transformer Recipe for a General, Powerful, Scalable Graph Transformer NeurIPS 2022
- GRPE: Relative Positional Encoding for Graph Transformer GRPE: Relative Positional Encoding for Graph Transformer
- KDD 2021: Large-scale graph representation learning with very deep GNNs and self-supervision Large-scale graph representation learning with very deep GNNs and self-supervision KDD 2021
Graph regression Predict each molecule’s HOMO-LUMO energy gap, a continuous quantum-chemical property calculated with density functional theory. The default 2D graph conversion uses OGB’s nine categorical atom and three categorical bond fields from SMILES. Evaluated by MAE.
Dataset details
Task
Predict each molecule’s HOMO-LUMO energy gap, a continuous quantum-chemical property calculated with density functional theory. Mean absolute error in electron volts is the official metric.
Data
PCQM4Mv2 contains 3,746,619 PubChemQC molecules. Training molecules also have DFT-optimized 3D structures, while SMILES describe the final optimized connectivity; test labels are held by OGB-LSC.
Features
The default 2D graph conversion uses OGB’s nine categorical atom and three categorical bond fields from SMILES. Coordinates are optional additional inputs for training molecules and are unavailable in the same form for blind test molecules.
Splits and evaluation
Molecules are ordered and split by PubChem compound identifier into 90% train, 2% validation, 4% test-dev, and 4% test-challenge. Despite an original intention to use scaffolds, the released split is CID-based because of a preprocessing bug.
Quirks and pitfalls
PCQM4Mv2 corrected an approximately 10% mismatch in PCQM4M between initial SMILES and gaps computed after geometry optimization, so the versions are not interchangeable. The CID split is not a scaffold split. Test-dev and test-challenge are distinct blind sets, and using supplied training coordinates creates an input-availability asymmetry.
Sources
SOTA Progression
new SOTA
matches SOTA at that time
other proposed result
other baseline result
Leaderboard
| # | Model | HOMO-LUMO gap MAE (eV) ↓ | MAE (↓), Validate | Paper |
|---|---|---|---|---|
| 1 | DeMol | 0.0603 | — | |
| 2 | TGT-At | 0.0671 | — | |
| 3 | TGT-At (+RDKit) | 0.0683 | — | |
| 4 | UniMol | 0.0693 | — | |
| 5 | Uni-Mol+ (+RDKit) | 0.0705 | — | |
| 6 | GPS | 0.0720 | — | |
| 7 | Transformer-M | 0.0772 | — | |
| 8 | GPS | 0.0778 | — | |
| 9 | GoMS_GT | 0.0780 | — | |
| 10 | Transformer-M | 0.0782 | — | |
| 11 | Transformer-M | 0.0787 | — | |
| 12 | GEM-2 | 0.0793 | — | |
| 13 | MPNNs | 0.0800 | — | |
| 14 | GEM-2 (+RDKit) | 0.0806 | — | |
| 15 | GEM-2 | 0.0806 | — | |
| 16 | GPTrans-L | 0.0809 | — | |
| 17 | GPTrans-B | 0.0813 | — | |
| 18 | GPTrans-S | 0.0823 | — | |
| 19 | ET+RRWP | 0.0832 | — | |
| 20 | GPTrans-T | 0.0833 | — | |
| 21 | GRIT 2-QiQRW | 0.0838 | — | |
| 22 | ET | 0.0840 | — | |
| 23 | GRIT (our run) | 0.0842 | — | |
| 24 | GPS-Deep | 0.0852 | — | |
| 25 | GraphGPS_DEEP | 0.0852 | — | |
| 26 | GraphGPS Medium | 0.0858 | — | |
| 27 | GTs | 0.0858 | — | |
| 28 | GPS-medium | 0.0858 | — | |
| 29 | GraphGPS_BASE | 0.0858 | — | |
| 30 | EGT-Larger | 0.0859 | — | |
| 31 | GRIT | 0.0859 | — | |
| 32 | OSAN | 0.0862 | — | |
| 33 | EGT | 0.0862 | — | |
| 34 | GraphGPS | 0.0862 | — | |
| 35 | Graphormer | 0.0864 | — | |
| 36 | GRPE-Large | 0.0867 | — | |
| 37 | GRPE_LARGE | 0.0867 | — | |
| 38 | EGT-Large | 0.0869 | — | |
| 39 | EGT | 0.0869 | — | |
| 40 | Graphormer | 0.0870 | — | |
| 41 | EGT-Large | 0.0872 | — | |
| 42 | GRPE-Large | 0.0876 | — | |
| 43 | GRPE | 0.0876 | — | |
| 44 | EGT-Medium | 0.0881 | — | |
| 45 | IDMPNN | 0.0882 | — | |
| 46 | GraphormerV2-L | 0.0883 | — | |
| 47 | GRPE-Standard | 0.0890 | — | |
| 48 | GRPE | 0.0890 | — | |
| 49 | GRPE_BASE | 0.0890 | — | |
| 50 | GRPE-Standard | 0.0898 | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time