SCOPE-BENCH / README.md
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metadata
tags:
  - molecular-property-prediction
  - out-of-distribution
  - QM9
  - graph-neural-networks
  - SCOPE-BENCH
license: cc-by-nc-4.0
pretty_name: SCOPE-BENCH
size_categories:
  - 100K<n<1M
language:
  - en

SCOPE-BENCH: Scaffold-Cluster Out-Of-Distribution Performance Evaluation Benchmark

SCOPE-BENCH is a rigorous out-of-distribution (OOD) benchmark for molecular property prediction. Unlike conventional scaffold splits, SCOPE-BENCH creates structurally disjoint source and target domains by clustering molecules based on physicochemical descriptors, blocking shortcut learning, and revealing true extrapolation abilities.

📊 Dataset Splits (As used in the NeurIPS 2026 paper)

Split Files Clusters Samples Purpose
Source (training) a0.csv, a1.csv, a6.csv, a8.csv, a9.csv, a11.csv 6 94,562 Supervised pre‑training + multi‑source adaptation pool
Validation a10.csv 1 18,326 Hyperparameter tuning
Target (test) a2.csv, a3.csv, a4.csv, a5.csv, a7.csv 5 19,894 Strict OOD evaluation (zero‑shot extrapolation)
Fine‑grained tasks scaffold_datasets1/*.csv (each ≥200 samples) 16 varies Independent zero‑shot evaluations (Table 2)

🔬 Molecular Properties

Each CSV file contains the following columns (based on the QM9 dataset):

Column Description Unit
SMILES Simplified molecular input line entry system
HOMO Highest Occupied Molecular Orbital energy eV
LUMO Lowest Unoccupied Molecular Orbital energy eV
GAP HOMO–LUMO gap (LUMO – HOMO) eV
(other columns) Additional QM9 properties (e.g., dipole moment, polarizability, etc.) various

🗂️ File Structure

SCOPE-BENCH/ ├── a0.csv ... a11.csv # 12 cluster files ├── scaffold_datasets1/ # 16 independent target scaffolds ├── README.md ├── dataset_info.json └── LICENSE