README.md

---
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