Datasets:

datamol-io
/

safe-gpt

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

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
SAFE-based models on Hugging Face:

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

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:

@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}
}