Create README.md

README.md

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