datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
smiles-transformers
smiles-transformers dataset
TODO: Add references to the datasets we curated
dataset features
name: text
Molecule SMILES : string
name: formula
Molecular formula : string
name: NumHDonors
Number of hidrogen bond donors : int
name: NumHAcceptors
Number of hidrogen bond acceptors : int
name: MolLogP
Wildman-Crippen LogP : float
name: NumHeteroatoms
Number of hetero atoms: int
name: RingCount
Number of rings : int
name: NumRotatableBonds
Number of rotable… See the full description on the dataset page: https://huggingface.co/datasets/maykcaldas/smiles-transformers.PubChem-124M-SMILES-SELFIES-InChI-IUPAC
PubChem-124M-Canonicalized-SELFIES-InChI-IUPAC
Dataset Summary
This dataset contains ~124 million chemical structures sourced from PubChem (as of Jan 2026), processed into a clean, machine-learning-ready Parquet format.
Unlike raw XML/JSON dumps or standard CSVs, this dataset provides a unified, tabular structure that joins multiple chemical identifiers and descriptors into a single sharded resource:
SMILES: Raw and RDKit-Canonicalized.
SELFIES: Pre-computed 100% robust… See the full description on the dataset page: https://huggingface.co/datasets/hheiden/PubChem-124M-SMILES-SELFIES-InChI-IUPAC.Smiles2Dockhttps://arxiv.org/pdf/2406.05738
chemnlp_iupac_smiles
Dataset Card for "chemnlp_iupac_smiles"
More Information needed
PubChem-124M-SMILES-SELFIES-InChI-IUPAC
PubChem-124M-Canonicalized-SELFIES-InChI-IUPAC
Dataset Summary
This dataset contains ~124 million chemical structures sourced from PubChem (as of Jan 2026), processed into a clean, machine-learning-ready Parquet format.
Unlike raw XML/JSON dumps or standard CSVs, this dataset provides a unified, tabular structure that joins multiple chemical identifiers and descriptors into a single sharded resource:
SMILES: Raw and RDKit-Canonicalized.
SELFIES: Pre-computed… See the full description on the dataset page: https://huggingface.co/datasets/Bilsteen/PubChem-124M-SMILES-SELFIES-InChI-IUPAC.smiles_transformer_shuffledzinc20_smiles
Dataset Card for "zinc20_smiles"
More Information needed
smiles-selfies-pretrainsmiles-molecules-chembl
ChEMBL Molecule Generation Dataset
Dataset Description
ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs.
Task Description
For both distribution learning-based and goal-oriented molecule generation. That is to generate new molecules that has desirable properties measured by some oracles.… See the full description on the dataset page: https://huggingface.co/datasets/antoinebcx/smiles-molecules-chembl.chembl-smiles-curated
Dataset Card for Curated ChEMBL SMILES Dataset (lovingscience/chembl-smiles-curated)
This dataset is a reproducibly cleaned, deduplicated, and split ChEMBL SMILES dataset built for training molecular generative models and chemical space mapping tools (such as Imolgen and Navifold).
Dataset Pipeline Provenance & Configuration
ChEMBL Release Version: 37
RDKit Version: 2026.03.6
Deduplication & Split Seed: 42
Split Ratios: Train (80.0%) / Validation (10.0%) / Test… See the full description on the dataset page: https://huggingface.co/datasets/lovingscience/chembl-smiles-curated.PubChem-124M-SMILES-SELFIES-InChI-IUPAC
PubChem-124M-Canonicalized-SELFIES-InChI-IUPAC
Dataset Summary
This dataset contains ~124 million chemical structures sourced from PubChem (as of Jan 2026), processed into a clean, machine-learning-ready Parquet format.
Unlike raw XML/JSON dumps or standard CSVs, this dataset provides a unified, tabular structure that joins multiple chemical identifiers and descriptors into a single sharded resource:
SMILES: Raw and RDKit-Canonicalized.
SELFIES: Pre-computed 100% robust… See the full description on the dataset page: https://huggingface.co/datasets/th-laurel/PubChem-124M-SMILES-SELFIES-InChI-IUPAC.ord_rxn_smiles_procedure
Dataset Details
Dataset Description
The open reaction database is a database of chemical reactions and their conditions
Curated by:
License: CC BY SA 4.0
Dataset Sources
original data source
Citation
BibTeX:
@article{Kearnes_2021,
doi = {10.1021/jacs.1c09820},
url = {https://doi.org/10.1021%2Fjacs.1c09820},
year = 2021,
month = {nov},
publisher = {American Chemical Society ({ACS})},
volume = {143},
number = {45},
pages =… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/ord_rxn_smiles_procedure.RCR_RP_57K_SMILES-MMChatReaction Condition Prediction Dataset (Reagent Prediction)
molecule representation format: 1D SMILES
will further encode into 2D graph features
For detail, refer to PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes: https://arxiv.org/pdf/2406.13193
enamine_smiles_datasetchembl-v37-smiles-curated
Dataset Card for Curated ChEMBL SMILES Dataset (lovingscience/chembl-smiles-curated)
This dataset is a reproducibly cleaned, deduplicated, and split ChEMBL SMILES dataset built for training molecular generative models and chemical space mapping tools (such as Imolgen and Navifold).
Dataset Pipeline Provenance & Configuration
ChEMBL Release Version: 37
RDKit Version: 2026.03.6
Deduplication & Split Seed: 42
Split Ratios: Train (80.0%) / Validation (10.0%) / Test… See the full description on the dataset page: https://huggingface.co/datasets/lovingscience/chembl-v37-smiles-curated.smiles-eval
SMILES eval
This is a dataset that measures LLM capabilities at generating SMILES chemical molecule representations from natural language descriptions.
It was generated by prompting Gemini 2.5 Pro for molecule description and SMILES pairs, and filtering for a) molecules valid according to rdkit and b) reliably regenerated by Gemini and c) removing duplicates using fuzzy matching.
Difficulty is estimated by how reliable Gemini 2.5 Pro is at generating the molecule.
This dataset was… See the full description on the dataset page: https://huggingface.co/datasets/eth-sri/smiles-eval.pubchem-smiles-molecular-formulachembl-2025-randomized-smiles-cleaned-rdkit-descriptorsta-ESM2
taxonomy_aware_ESM2
This repository implements a Taxonomy-Aware Protein Function Prediction model. It synergizes the structural language understanding of ESM2 (Evolutionary Scale Modeling) with explicit phylogenetic lineage information.
smiles-2025enamine_smiles_datasetMolInst_RS_125K_SMILES-MMChatRCR_SP_70K_SMILES-MMChatReaction Condition Prediction Dataset (Solvent Prediction)
molecule representation format: 1D SMILES
will further encode into 2D graph features
For Detail, refer to PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes: https://arxiv.org/pdf/2406.13193
MolInst_FS_125K_SMILES-MMChatSMol_RS_Filtered_825K_SMILES-MMChatRetrosynthesis Prediction Dataset (derived from SMolInstruct)
molecule representation format: 1D SMILES
will further encode into 2D graph features
We filtered out overlapping samples from the original train-split (test-set: MolInstruct-Retrosynthesis Prediction)
We only include single-step retrosynthesis prediction.
For Detail, refer to PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes: https://arxiv.org/pdf/2406.13193
RCR_CP_10K_SMILES-MMChatReaction Condition Prediction Dataset (Catalyst Prediction)
molecule representation format: 1D SMILES
will further encode into 2D graph features
Detail refer to PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes: https://arxiv.org/pdf/2406.13193
MolInst_RS_125K_Scaffold_SMILES-MMChatRetrosynthesis Prediction Dataset (derived from MolInstruct)
molecule representation format: 1D SMILES
will further encode into 2D graph features
We use scaffold splitting to reconstruct the train-split. We use SMolInstruct RS train split as the sample pool.
We only include single-step retrosynthesis prediction.
For Detail, refer to PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes: https://arxiv.org/pdf/2406.13193
pubchem_and_chembl-2025-randomized-smiles-cleanedDrug_Protein_Interactions_SmilesSMol_FS_Filtered_875K_SMILES-MMChatForward Reaction Prediction Dataset (derived from SMolInstruct)
molecule representation format: 1D SMILES
will further encode into 2D graph features
We filtered out overlapping samples from original train-split (test-set: MolInstruct-Forward Reaction Prediction)
For Detail, refer to PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes: https://arxiv.org/pdf/2406.13193