datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pubchemqc-pm6The PubChemQC PM6 dataset consists of optimized molecular geometries and electronic properties calculated by the PM6 method for 94.0% of the 91.6 million molecules cataloged in PubChem Compounds retrieved on August 29, 2016. In addition to neutral states, the cationic, anionic, and spin flipped electronic states of respectively 56.2%, 49.7%, and 41.3% of the molecules have also been studied. As such, the grand total of the PM6 calculations amounted to 221 million.pubchemqc-b3lypThe presented “PubChemQC B3LYP/6-31G*//PM6” data set is composed of the electronic properties of 85,938,443 molecules, encompassing a broad spectrum of molecules from essential compounds to biomolecules with a molecular weight up to 1000. These molecules account for 94.0% of the original PubChem Compound catalog as of August 29, 2016. The electronic properties, including orbitals, orbital energies, total energies, dipole moments, and other pertinent properties, were computed by using the B3LYP/6-31G* and PM6 methods.SimNMR-PubChem
SimNMR-PubChem Database
🧬 Overview
The SimNMR-PubChem Database provides a comprehensive collection of 106 million molecules from PubChem, each paired with corresponding simulated NMR spectra.
The dataset has been carefully preprocessed and optimized for NMR spectral analysis, molecular structure elucidation, and machine learning applications in computational chemistry.
📁 Dataset Structure
The database is organized into two main components:
Metadata… See the full description on the dataset page: https://huggingface.co/datasets/yqj01/SimNMR-PubChem.PubChemQH
Introduction
Welcome! This is the dataset used in the ICML'25 paper Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity and ICLR'24 paper Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonian for Scalable Molecular Systems.
For more details, please refer to the code repository. If you need to use the PubChem QH dataset, please cite these two articles.
Citation
@InProceedings{pmlr-v267-luo25l,
title =… See the full description on the dataset page: https://huggingface.co/datasets/EperLuo/PubChemQH.PubChem-rawPubChemQCR
PubChemQCR
Description
PubChemQCR dataset contains the relaxation trajectory of ~3.5 million small molecules, which can facilitate the development of machine learning interatomic potential (MLIP) models. The relaxation is performed sequentially using PM3, Hartree-Fock, and DFT methods, resulting in a total of 300 million snapshots, 105 million of which are computed using DFT. The dataset is split into two portions, a subset and a full set. Both sets share the same… See the full description on the dataset page: https://huggingface.co/datasets/divelab/PubChemQCR.PubChemPubChem-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.PubChemPubChem-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.pubchem-10m-mixed-80-20pubchem-selfies-pretrainpubchem-10-15-2024PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the `standardize` option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.pubchem-30m-mixed-80-20pubchem-04-18-2025PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the `standardize` option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.pubchem_bioassay
Dataset Card for "pubchem_bioassay"
More Information needed
pubchem-10m-canonicalized
dataset description
We downloaded PubChem-10m dataset from here and canonicalized it.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
from rdkit import Chem
def canonicalize(mol):
mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
return mol
We randomly split the preprocessed data into train and validation. The ratio is 9 : 1.
pubchem-faiss-library
PubChem-Scale Molecule Retrieval Library (FAISS)
This dataset provides a prebuilt, global FAISS retrieval library for molecule search at PubChem scale.
It includes molecular metadata (SMILES, formula, exact mass), precomputed embeddings, and FAISS indices.
Contents
index_smi.faiss: FAISS HNSW index built on SMI-TED embeddings (vectors_smi.npy).
index_chem.faiss: FAISS HNSW index built on ChemBERTa embeddings (vectors_chem.npy).
vectors_smi.npy: SMI-TED embeddings for… See the full description on the dataset page: https://huggingface.co/datasets/YinkaiW/pubchem-faiss-library.pubchem_selfiesThis dataset contains ~100M molecules from PubChem, with their SMILES and SELFIES representations.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.PubChem10M_SELFIESPubChem10M dataset by DeepChem encoded to SELFIES using group-selfies.
PubChem-actInPubChemSFT
all_clean.json
remove overlapped parts with ChEBI-20 test
remove no description SMILES
Format:{
SMILES <str>:
[
["Please describe the molecule", DESCRIPTION],
...,
]
}
Stats
max tokens length: 6113
min tokens length: 20
mean tokens length: 191
median tokens length: 149
Total 326,689 single turn dialogue. Total 293,302 SMILES examples.
Size: Train: 264,391 Valid: 33,072 Test: 32,987
conversation template
'conversation':{
[
"from": "human",
"value": <QUERY>, #… See the full description on the dataset page: https://huggingface.co/datasets/IDEA-AI4S/PubChemSFT.pubchem-smiles-molecular-formulaPubChemQAPubChemQA is released for Molecule Question-Answering (MQA) task.
For technical details, please refer to BioMedGPT.
If you find our work useful in your research, please consider citing:
@article{luo2024biomedgpt,
title={Biomedgpt: An open multimodal large language model for biomedicine},
author={Luo, Yizhen and Zhang, Jiahuan and Fan, Siqi and Yang, Kai and Hong, Massimo and Wu, Yushuai and Qiao, Mu and Nie, Zaiqing},
journal={IEEE Journal of Biomedical and Health Informatics}… See the full description on the dataset page: https://huggingface.co/datasets/PharMolix/PubChemQA.pubchem-02-21-2025PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the `standardize` option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.PubChem_Compound_TOC_70Mpubchem_10m_genmol_similarity
PubChem 10M GenMol Fingerprint Similarity Dataset
This dataset is an augmented version of the PubChem 10M dataset, enhanced with molecular similarity data generated using GenMol, as described in the paper GenMol: A Drug Discovery Generalist with Discrete Diffusion.
The dataset contains molecular structures represented as SMILES strings along with their corresponding molecular fingerprints, similarity scores, and various molecular properties.
This dataset is for training a Chem-MRL… See the full description on the dataset page: https://huggingface.co/datasets/Derify/pubchem_10m_genmol_similarity.pubchem-07-23-2024PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the `standardize` option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.PubChem_Drug_Instruction_Tuning