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---
dataset_info:
features:
- name: SMILES
dtype: string
- name: Deep SMILES
dtype: string
- name: SELFIES
dtype: string
- name: SAFE
dtype: string
splits:
- name: train
num_bytes: 605949364569
num_examples: 1485280171
- name: valid
num_bytes: 1248532124
num_examples: 2999216
- name: test
num_bytes: 1264493396
num_examples: 2999132
download_size: 241151459346
dataset_size: 608462390089
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
size_categories:
- 1B<n<10B
---
# ZINC_22 Pretraining Dataset
## Dataset Description
This dataset is derived from the **ZINC-22** database (~70B synthesizable compounds as of Sept 2024) and was prepared for large-scale pretraining of molecular language models. We randomly sampled **1.5 billion molecules** using a **stratified heavy-atom count split** (4–49 atoms) to ensure coverage of diverse chemical sizes.
All molecules were **deduplicated** to remove repeats, **canonicalized** in SMILES format, and **converted** into multiple string representations: SMILES, SELFIES, SAFE, DeepSMILES.
---
## Precomputed Statistics
This repository includes precomputed reference statistics (`*_stats.pkl`) for evaluating generated molecules against validation and test sets.
These statistics are used to compute the following metrics:
- **FCD** – Fréchet ChemNet Distance
- **SNN** – Similarity to Nearest Neighbor
- **Frag** – Fragment similarity (BRICS decomposition)
- **Scaf** – Scaffold similarity (Bemis–Murcko scaffolds)
### File Naming Convention
Files are provided for multiple reference set sizes:
- `_175k` → 175,000 molecules
- `_500k` → 500,000 molecules
- `_1M` → 1 million molecules
- `_3M` → 3 million molecules
- *(no suffix)* → full set
By convention:
- `valid_stats_*` → computed from the **random validation split**
- `test_stats_*` → computed from the **scaffold-based split**
These statistics enable **consistent and reproducible** evaluation across experiments.
---
## How to Use
Before running the example below, make sure you have these packages installed:
```bash
pip install rdkit fcd-torch
```
### Example: Download stats from the Hub and compute FCD
```python
from huggingface_hub import hf_hub_download
import pickle
from fcd_torch import FCD as FCDMetric
# 1. Download the precomputed stats file from Hugging Face Hub
stats_path = hf_hub_download(
repo_id="chandar-lab/ZINC_22",
repo_type="dataset",
filename="valid_stats_175k.pkl" # change to desired file
)
# 2. Load the reference stats
with open(stats_path, "rb") as f:
reference_stats = pickle.load(f)
# 3. Compute FCD for your generated molecules
generated_smiles = ["CCO", "CCN", "CCCN", "CCCN"] # replace with your generated set
fcd_calculator = FCDMetric(batch_size=4)
fcd_value = fcd_calculator(gen=generated_smiles, pref=reference_stats["FCD"])
print(f"FCD score: {fcd_value:.4f}")
```
## Citation
```bibtex
@misc{chitsaz2025novomolgenrethinkingmolecularlanguage,
title={NovoMolGen: Rethinking Molecular Language Model Pretraining},
author={Kamran Chitsaz and Roshan Balaji and Quentin Fournier and Nirav Pravinbhai Bhatt and Sarath Chandar},
year={2025},
eprint={2508.13408},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.13408},
}
``` |