README.md

---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - chemistry
  - molecules
  - smiles
  - safe
  - drug-discovery
size_categories:
  - 1B<n<10B
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/*.parquet
      - split: validation
        path: data/validation/*.parquet
      - split: test
        path: data/test/*.parquet
---

# SAFE Molecules Dataset (v2)

A large-scale molecular dataset containing approximately **1.17 billion unique molecules**, each represented with both **canonical SMILES** and **SAFE (Sequential Attachment-based Fragment Embedding)** strings.
This dataset is intended to support **large-scale pretraining and evaluation of chemical language models**, including generative, conditional, and structure-aware modeling tasks.

> **Note**
> This is **version 2** of the SAFE dataset. The original v1 release contained invalid SAFE strings and is archived for reproducibility at
> [https://huggingface.co/datasets/datamol-io/safe-gpt/tree/b83175cd7394](https://huggingface.co/datasets/datamol-io/safe-gpt/tree/b83175cd7394)


## SAFE Representation

SAFE (Sequential Attachment-based Fragment Embedding) is a **fragment-based molecular string representation** that encodes molecules as **sequences of chemically meaningful fragments together with their attachment structure**.

In SAFE, molecules are decomposed into fragments using rule-based fragmentation, and the resulting fragments are arranged into a **deterministic sequence** that explicitly represents how fragments are connected. The representation is **fully reversible**, allowing exact reconstruction of the original molecular graph.

By operating at the **fragment level** rather than the atom level (as in SMILES), SAFE reduces syntactic fragility and naturally supports both **unconstrained molecular generation** and **structure-constrained tasks** (e.g., scaffold or fragment conditioning) using standard sequence models.

Additional resources:

* **SAFE GitHub repository**: [https://github.com/datamol-io/safe](https://github.com/datamol-io/safe)
* **SAFE-based models on Hugging Face**:
  * [SAFE-GPT 87M](https://huggingface.co/datamol-io/safe-gpt)
  * [NovoMolGen 32M-BPE](https://huggingface.co/bisectgroup/NovoMolGen_32M_SAFE_BPE)
  * [NVIDIA's GenMol 89M](https://huggingface.co/nvidia/NV-GenMol-89M-v2)


## Dataset Description

The dataset aggregates molecules from two major public chemical resources:

* **ZINC20**: ~1.0 billion commercially available, purchasable compounds
* **UniChem**: ~188 million compounds aggregated from multiple public databases

After standardization and deduplication, the dataset contains **~1.17 billion unique molecules**.

Each molecule is provided with:

* `mol_id`: Source-specific molecule identifier
* `smiles`: Canonical SMILES string
* `safe`: Canonical SAFE string representation (BRICS-based fragmentation)
* `source`: Origin of the molecule (`zinc20` or `unichem`)

Due to the scale of the dataset, **streaming access is recommended** for most use cases.


## Dataset Splits

| Split      | Molecules | Proportion |
| ---------- | --------- | ---------- |
| Train      | ~933M     | 80%        |
| Validation | ~117M     | 10%        |
| Test       | ~117M     | 10%        |


## Usage Example

```python
from datasets import load_dataset

# Load dataset (streaming recommended)
dataset = load_dataset("datamol-io/safe-gpt", streaming=True)

train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]
```

---

## Citation

If you use this dataset or the SAFE representation, please cite the SAFE paper:

```bibtex
@article{noutahi2024gotta,
  title={Gotta be SAFE: a new framework for molecular design},
  author={Noutahi, Emmanuel and Gabellini, Cristian and Craig, Michael and Lim, Jonathan SC and Tossou, Prudencio},
  journal={Digital Discovery},
  volume={3},
  number={4},
  pages={796--804},
  year={2024},
  publisher={Royal Society of Chemistry}
}
```