15,535
Graphs
~151
Avg Nodes
10
Node Features
Multi-label
Task
What Peptides-func contains
Peptides-func is a dataset of 15,535 peptide molecular graphs from the Long Range Graph Benchmark (LRGB). Each peptide is represented as an atomic graph: individual atoms are nodes (10 features encoding atom type, degree, charge, etc.), and chemical bonds are edges. Graphs average 151 atoms and 307 bonds. The multi-label task predicts peptide functional classes (antimicrobial, antiviral, cell-penetrating, etc.).
The defining characteristic is that functional properties depend on the overall peptide structure, not just local motifs. An antimicrobial peptide's activity depends on the arrangement of hydrophobic and charged atoms across its full length. Information must propagate from one end of the chain to the other -- a distance of many hops in the atomic graph.
Why Peptides-func matters
Standard GNN benchmarks (Cora, MUTAG, ZINC) can be solved with 2-3 layers of message passing (2-3 hop neighborhoods). The LRGB authors showed that most GNN improvements on these benchmarks do not transfer to tasks requiring long-range information. Peptides-func exposes this limitation: a 2-layer GCN can only aggregate information from the nearest 2 amino acids, missing the global structural patterns that determine function.
Graph transformers (GPS, SAN) address this by adding global attention: each node can attend to every other node regardless of graph distance. On Peptides-func, GPS achieves ~65% average precision versus GCN's ~59%. The 6-point gap, while modest in absolute terms, represents a qualitative capability difference -- the ability to reason about distant graph relationships.
Loading Peptides-func in PyG
from torch_geometric.datasets import LRGBDataset
train_dataset = LRGBDataset(root='/tmp/LRGB', name='Peptides-func', split='train')
val_dataset = LRGBDataset(root='/tmp/LRGB', name='Peptides-func', split='val')
test_dataset = LRGBDataset(root='/tmp/LRGB', name='Peptides-func', split='test')
print(f"Train: {len(train_dataset)}")
graph = train_dataset[0]
print(f"Nodes: {graph.num_nodes}, Edges: {graph.num_edges}")
print(f"Labels: {graph.y.shape}") # Multi-label binary vectorLRGB datasets use average precision (AP) as the primary metric for multi-label tasks.
Common tasks and benchmarks
Multi-label graph classification evaluated by average precision (AP). GCN: ~59.3%, GIN: ~59.8%, GAT: ~59.6%, GatedGCN: ~60.8%, GPS: ~65.4%, SAN: ~64.4%. The clear separation between standard GNNs (~59-61%) and graph transformers (~64-65%) validates the long-range hypothesis: global attention is necessary for tasks where information must travel many hops.
Example: long-range effects in business
Long-range dependencies are everywhere in enterprise data. A customer's purchase 6 months ago affects their churn risk today. A supplier disruption in Asia impacts manufacturing in Europe weeks later. A product return triggers a chain of events across customer service, inventory, and finance. These long-range relational effects cannot be captured by models that only see immediate neighbors. They require the global context that graph transformers provide.
Published benchmark results
Multi-label graph classification on Peptides-func. Metric is average precision (AP). Higher is better.
| Method | AP (%) | Year | Paper |
|---|---|---|---|
| GCN | 59.3 | 2022 | Dwivedi et al. |
| GIN | 59.8 | 2022 | Dwivedi et al. |
| GAT | 59.6 | 2022 | Dwivedi et al. |
| GatedGCN | 60.8 | 2022 | Dwivedi et al. |
| SAN | 64.4 | 2022 | Dwivedi et al. |
| GPS | 65.4 | 2022 | Rampasek et al. |
| Exphormer | ~65.0 | 2023 | Shirzad et al. |
Original Paper
Long Range Graph Benchmark
V. P. Dwivedi, L. Rampasek, M. Galkin, A. Parviz, G. Wolf, A. T. Luu, D. Beaini (2022). NeurIPS Datasets and Benchmarks Track
Read paper →Original data source
The Peptides-func dataset is part of the Long Range Graph Benchmark (LRGB). The peptide structures come from the SATPdb database of therapeutic peptides. The benchmark is available from the LRGB GitHub repository.
@inproceedings{dwivedi2022long,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampasek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
booktitle={NeurIPS Datasets and Benchmarks Track},
year={2022}
}BibTeX citation for the LRGB benchmark (Peptides-func dataset).
Which dataset should I use?
Peptides-func vs Peptides-struct: Both are from LRGB. Peptides-func is multi-label classification (function). Peptides-struct is regression (structural properties). Func tests long-range classification; struct tests long-range regression.
Peptides-func vs ZINC: ZINC tests expressiveness on small molecules (~23 atoms) with local features. Peptides-func tests long-range capture on larger molecules (~151 atoms). If your model works on ZINC but fails on Peptides-func, it lacks long-range capability.
Peptides-func vs QM9: QM9 has small molecules where 3D geometry dominates. Peptides-func has large peptides where long-range graph topology dominates. Different bottlenecks, both molecular.
From benchmark to production
Production long-range graph reasoning operates on much larger graphs (millions of nodes) where global attention is computationally expensive. Efficient approximations (sparse attention, local-global hybrid attention, memory-efficient transformers) are required. The long-range capability must be maintained while scaling to production graph sizes.