metadata
language: []
pretty_name: PCQM4Mv2 3D
config_name: pcqm4mv2-3d
dataset_size: 562677714
size_categories: n>1M
license: mit
license_link: https://github.com/snap-stanford/ogb/blob/master/LICENSE
tags:
- graphs
- molecules
- chemistry
- parquet
- torch-geometric
- ogb
task_categories:
- graph-regression
homepage: https://ogb.stanford.edu/docs/lsc/pcqm4mv2/
pcqm4mv2-3d
PCQM4Mv2 train split with 3D conformations aligned to OGB indices for HOMO-LUMO regression.
License: MIT License (OGB)
Splits
| Split | Rows | File | Size (MB) |
|---|---|---|---|
| train | 3,195,733 | train.parquet |
562.68 |
Features
- mol_id: int64 unique identifier per molecule
- x: list[int64[9]], shape (num_nodes, 9), atom feature vector
- edge_index: int64[2, num_edges], COO adjacency
- edge_attr: list[int64[3]], shape (num_edges, 3), bond features
- pos: list[float32[3]], shape (num_nodes, 3), 3D coordinates
- num_nodes: int64 number of atoms
- smiles: string canonical SMILES
- target: float32 HOMO-LUMO gap
Citation
@article{hu2021ogblsc,
title={OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs},
author={Hu, Weihua and Fey, Matthias and Ren, Hongyu and Nakata, Maho and Dong, Yuxiao and Leskovec, Jure},
journal={arXiv preprint arXiv:2103.09430},
year={2021}
}