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--- |
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tags: |
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- chemistry |
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- medical |
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- biology |
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language: |
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- en |
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pretty_name: MolPILE dataset |
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size_categories: |
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- 100M<n<1B |
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--- |
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# MolPILE dataset |
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// link to preprint |
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## Description |
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MolPILE is a large-scale molecular dataset designed for pretraining and evaluating machine learning models in cheminformatics. |
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It is compiled from several major chemical databases: UniChem, PubChem, Mcule, ChemSpace, SuperNatural3, and COCONUT. Workflow |
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included preprocessing, standardization, and feasibility filtering of molecules. |
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## Data dictionary |
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Columns and meaning: |
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- `source` |
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Source dataset identifier. Combined with `id` column, it provides full traceability to the original data entry. |
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Possible values: `PubChem`, `UniChem`, `Mcule`, `ChemSpace`, `SuperNatural3`, `COCONUT` |
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- `id` |
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A unique identifier for the molecule, retained from its source database (e.g., Mcule ID, PubChem CID). |
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Example: `MCULE-7212330550` |
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- `SMILES`: |
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Canonical SMILES representation of the molecule, after RDKit processing. |
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Example: `O=C(O)C1=CC=CC(O)C1O` |
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## Statistics |
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| **Descriptor** | **Value** | |
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|:----------------------:|:-----------:| |
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| Molecules count | 221,950,487 | |
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| Median SAScore | 3.05 | |
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| #Circles | 6,422,057 | |
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| Bemis-Murcko scaffolds | 3,620,809 | |
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| Ertl functional groups | 128,347 | |
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| Salts | 1,089,501 | |
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## Workflow |
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## Loading the MolPILE dataset |
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```python |
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import polars as pl |
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from huggingface_hub import hf_hub_download |
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parquet_file_path = hf_hub_download( |
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repo_id="scikit-fingerprints/MolPILE", |
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repo_type="dataset", |
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filename="molpile.parquet", |
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local_dir="datasets", |
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local_dir_use_symlinks=False |
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) |
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df = pl.read_parquet(parquet_file_path) |
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print(df.head()) |
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``` |
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## Licenses |
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MolPILE is a collection of processed datasets, not a single dataset. It is shared as a single Parquet file only for convenience. |
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Individual sources are separated by `source` and `id` columns. Each source has its own separate license, which we list below. |
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As its entirety, MolPILE does not have a single license, as it is a collection, not a single dataset. |
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Users interested in that can easily filter the dataset by source. |
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In case of PubChem and UniChem, users may also want to check the individual licenses of their sources. |
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Users using those sources are also asked to cite the appropriate publications, which we provide below. |
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We do not make any claims about licensing of models trained on MolPILE, nor put any additional limitations. |
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- UniChem: CC0 (public domain); Chambers, Jon, et al. "UniChem: a unified chemical structure cross-referencing and identifier tracking system." Journal of Cheminformatics 5.1 (2013): 3. |
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- PubChem: CC-BY-4.0; Kim, Sunghwan, et al. "PubChem 2023 update." Nucleic Acids Research 51.D1 (2023): D1373-D1380. |
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- Mcule: CC-BY-NC-4.0; Kiss, Robert, Mark Sandor, and Ferenc A. Szalai. "http://Mcule. com: a public web service for drug discovery." Journal of Cheminformatics 4.Suppl 1 (2012): P17. |
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- ChemSpace: CC-BY-NC-4.0; please cite link https://chem-space.com/compounds/screening-compound-catalog |
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- SuperNatural3: not specified, only "freely available"; Gallo, Kathleen, et al. "SuperNatural 3.0—a database of natural products and natural product-based derivatives." Nucleic Acids Research 51.D1 (2023): D654-D659. |
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- COCONUT: CC0 (public domain); Sorokina, Maria, et al. "COCONUT online: collection of open natural products database." Journal of Cheminformatics 13.1 (2021): 2. |
