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