Molecular Datasets
Collection
Small-molecule and polymer datasets. Start from molecular-datasets-index. https://liugangcode.github.io/molecular-dataset-hub/ • 100 items • Updated • 1
Datasets:
CheMixHub miscible-solvent
CheMixHub data for simulated density, mixing, and vaporization properties of solvent mixtures, with component molecular structures, mixture composition, source conditions, and property labels. Each label describes the complete mixture.
Part of the Molecular Datasets collection.
Get started
from datasets import load_dataset
dataset = load_dataset("liuganghuggingface/moldata-chemixhub_miscible_solvent", "default")
Where the data comes from
- Original data: published with the original study
- Original study: Miscible solvent dataset
- Redistributed by: CheMixHub (dataset page)
Tables
| Config | Splits | Rows | Targets |
|---|---|---|---|
default |
full | 90,183 | value (regression; units not specified) |
data/ contains rows with readable molecular structures; identical observations are collapsed. observations/ contains all source rows.
Source label columns are text, including values such as <10. SMILES_original retains the input representation; SMILES_canonical provides a canonical representation. structure_key groups molecular components, and structure_status records parsing outcomes.
Data quality
- computed: Labels are computed (for example DFT or molecular dynamics).
- many sources: Compiled from several labs or papers, whose protocols differ.
- all structures readable: All 90,183 SMILES were read with RDKit.
- 8.9% undefined stereo: 8.9% of molecules have a stereocenter whose configuration is not given, so mirror-image forms cannot be told apart.
- 218 of 225 stereoisomer sets differ: Found 225 groups of molecules that differ only in stereochemistry. In 218, the values are 10-fold apart (log-scale labels), more than one standard deviation apart, or in different classes. That can be a real effect or a mistake in the source; a model that ignores stereochemistry treats these molecules as the same.
- 364 extreme values: 364 of 90,183 values lie more than 6 robust standard deviations from the median of their target (on a log scale when values span 1,000-fold or more). They are often typing or unit mistakes, and sometimes real; look at them before training.
- 19,157 molecules per task: A typical task has 19,157 labeled molecules.
- no source split: Make your own split. A random split puts near-identical molecules in both training and test sets, so results look better than they will on new compounds. Splitting by scaffold, or by date when dates are given, gives a more realistic estimate.
Dataset notes
- A component alone is not an independently measured molecule: labels apply to the whole mixture and its conditions.
- Composition, temperature, pressure, and units describe each mixture measurement.
- Components include ions and polymer repeat units.
- Density/mixing/vaporization labels are molecular-dynamics simulation results, not wet-lab experiments.
- License discrepancy between paper and Croissant is unresolved.
License
not_verified: the original paper lists CC BY-NC 4.0 in Appendix A1; the included Croissant metadata lists CC BY 4.0.
Citation
Cite the original study and data source:
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