|
|
--- |
|
|
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}, |
|
|
} |
|
|
``` |
