datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Open_Molecular_Crystals_2025_OMC25_train
Cite this dataset Gharakhanyan, V., Barroso-Luque, L., Yang, Y., Shuaibi, M., Michel, K., Levine, D. S., Dzamba, M., Fu, X., Gao, M., Liu, X., Ni, H., Noori, K., Wood, B. M., Uyttendaele, M., Boromand, A., Zitnick, C. L., Marom, N., Ulissi, Z. W., and Sriram, A. Open Molecular Crystals 2025 OMC25 train. ColabFit, 2025. https://doi.org/None
This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Open_Molecular_Crystals_2025_OMC25_train.moleculariq-v0.0SMARCA2_VHL_MolecularGlue_Designs_6HAY
SMARCA2–VHL Molecular Glue Designs (PDB 6HAY)
884 small molecules generated de novo by the Technetium TC-43.ai engine (GA-II), conditioned on the SMARCA2 bromodomain–VHL interface of the ternary complex 6HAY (2.24 Å), so that one molecule spans both partners.
Each molecule was constructed against this pocket rather than selected from a compound library — docking (AutoDock Vina) came afterwards, to place and score the generated molecules in the site. Each is supplied as a… See the full description on the dataset page: https://huggingface.co/datasets/Tc-43/SMARCA2_VHL_MolecularGlue_Designs_6HAY.dataset-CoT-Molecular-Biology-71pubchem-smiles-molecular-formulaOpen_Molecular_Crystals_2025_OMC25_validation
Cite this dataset Gharakhanyan, V., Barroso-Luque, L., Yang, Y., Shuaibi, M., Michel, K., Levine, D. S., Dzamba, M., Fu, X., Gao, M., Liu, X., Ni, H., Noori, K., Wood, B. M., Uyttendaele, M., Boromand, A., Zitnick, C. L., Marom, N., Ulissi, Z. W., and Sriram, A. Open Molecular Crystals 2025 OMC25 validation. ColabFit, 2025. https://doi.org/None
This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Open_Molecular_Crystals_2025_OMC25_validation.molecular-odor-dataset
Molecular Odor Dataset
Merged and deduplicated olfactory data from the GoodScents and Leffingwell databases,
accessed via Pyrfume. Each row is one molecule with its SMILES
string and binary labels for the 50 most frequent odor descriptors.
Statistics
Property
Value
Total molecules
5,308
Labels
50
Train split
4,220 molecules (79.5%)
Val split
543 molecules (10.2%)
Test split
545 molecules (10.3%)
Splitting strategy
Iterative stratified multi-label… See the full description on the dataset page: https://huggingface.co/datasets/Hari5115/molecular-odor-dataset.molecular-depiction-pairs-20k
Molecular depiction pairs, 20K
39,891 synthetic depictions of 19,990 drug-like PubChem molecules, each paired with the molecular identity it was rendered from, plus precomputed embeddings and fingerprints.
This is the development-scale dataset from molecular-depiction-alignment, published so the experiments in that repository can be reproduced without standing up the generation environment.
Why this exists
Not because a synthetic depiction corpus is novel. It is… See the full description on the dataset page: https://huggingface.co/datasets/hheiden/molecular-depiction-pairs-20k.moleculariq-trainPoolafrica-synth-cancer-cancer-genomics-molecular-africa-all
Cancer Genomics Molecular Africa | Africa (Electric Sheep Africa metadata inventory)
Size category: 1K<n<10K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health datasets help… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-cancer-cancer-genomics-molecular-africa-all.my_md_resultsmolecular-generation-datasetmoleculariq_arxivcorpus1neuronalTTR_targetsTranscriptionActivators_molecularFeaturesThe neuronalTTR_targetsTranscriptionActivators_molecularFeatures dataset is a part from the study “Comparative analysis of computational approaches for predicting Transthyretin (TTR) transcription activators and human dopamine D1 receptor antagonists”
https://doi.org/10.48550/arXiv.2506.01137
A total of 3,041 unique small molecule samples are included in this dataset. The samples are classified by their TTR transcription activity, resulting in 1,093 activators and 1,948 non-activators. This… See the full description on the dataset page: https://huggingface.co/datasets/ivanovaml/neuronalTTR_targetsTranscriptionActivators_molecularFeatures.human_dopamine_D1_receptor_antagonists_SMILES_13C_NMR_spectrosopy_with_molecular! A note: To address the size limitations on Hugging Face, only 200 of the 59,609 rows were uploaded. The full dataset is available upon request for interested parties
The human_dopamine_D1_receptor_antagonists_SMILES_13C_NMR_spectrosopy_with_molecular_features dataset is a part of the study " Comparative analysis of computational approaches for predicting Transthyretin (TTR) transcription activators and human dopamine D1 receptor antagonists"
https://doi.org/10.48550/arXiv.2506.01137
The… See the full description on the dataset page: https://huggingface.co/datasets/ivanovaml/human_dopamine_D1_receptor_antagonists_SMILES_13C_NMR_spectrosopy_with_molecular.molecular_papers_metadataCHOP_inhibitors_molecular_featuresThe CHOP_inhibitors_molecular_features is a part of the study "Predicting Nanoparticle Effects on Small Biomolecule Functionalities Using the Capability of Scikit-learn and PyTorch: A Case Study on Inhibitors of the DNA Damage-Inducible Transcript 3 (CHOP)"
https://doi.org/10.48550/arXiv.2504.09537
This dataset contains 19,504 rows, each representing a unique small molecule sample. Of these, 7,909 are CHOP inhibitors and 11,592 are not CHOP inhibitors. It includes 8 columns, which are… See the full description on the dataset page: https://huggingface.co/datasets/ivanovaml/CHOP_inhibitors_molecular_features.matched_molecular_pair
Matched Molecular Pair Dataset
This dataset is part of the Deep Principle Bench collection.
Files
matched_molecular_pair.csv: Main dataset file
Usage
import pandas as pd
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("yhqu/matched_molecular_pair")
# Or load directly as pandas DataFrame
df = pd.read_csv("hf://datasets/yhqu/matched_molecular_pair/matched_molecular_pair.csv")
Citation
Please cite this work if you… See the full description on the dataset page: https://huggingface.co/datasets/yhqu/matched_molecular_pair.Molecular-machine-learning-project
MML Final Project: Polymer Property Prediction Artifacts
Team PolyPredictors
This dataset contains the model weights and imputed datasets for our final project benchmarking AIMNet-X2D and Mamba.
Contents
models/: AIMNET folder for AIMNET-X2D, MAMBA folder for MAMBA (Model except multi_5props_best.pthwas trained for predict sparse dataset).
results/:
multi_5props_predictions.csv: Final predictions for the test set.
*filled_nan.csv: Training datasets with missing values… See the full description on the dataset page: https://huggingface.co/datasets/lck0328/Molecular-machine-learning-project.Molecular_formula_10Molecular_Function