<h1 align="center">
chembl_downloader
</h1>
<p align="center">
<a href="https://pypi.org/project/chembl_downloader">
<img alt="PyPI" src="https://img.shields.io/pypi/v/chembl_downloader" />
</a>
<a href="https://pypi.org/project/chembl_downloader">
<img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/chembl_downloader" />
</a>
<a href="https://github.com/cthoyt/chembl_downloader/blob/main/LICENSE">
<img alt="PyPI - License" src="https://img.shields.io/pypi/l/chembl_downloader" />
</a>
<a href="https://zenodo.org/badge/latestdoi/390113187">
<img src="https://zenodo.org/badge/390113187.svg" alt="DOI" />
</a>
<a href="https://github.com/psf/black">
<img src="https://img.shields.io/badge/code%20style-black-000000.svg" alt="Code style: black" />
</a>
<a href='https://chembl-downloader.readthedocs.io/en/latest/?badge=latest'>
<img src='https://readthedocs.org/projects/chembl-downloader/badge/?version=latest' alt='Documentation Status' />
</a>
</p>
Don't worry about downloading/extracting ChEMBL or versioning - just use ``chembl_downloader`` to write code that knows
how to download it and use it automatically.
Install with:
```bash
$ pip install chembl-downloader
```
Full technical documentation can be found on
[ReadTheDocs](https://chembl-downloader.readthedocs.io). Tutorials can be found
in Jupyter notebooks in the [notebooks/](notebooks/) directory of the
repository.
## Database Usage
### Download A Specific Version
```python
import chembl_downloader
path = chembl_downloader.download_extract_sqlite(version='28')
```
After it's been downloaded and extracted once, it's smart and does not need to download again. It gets stored
using [`pystow`](https://github.com/cthoyt/pystow) automatically in the `~/.data/chembl`
directory.
We'd like to implement something such that it could load directly into SQLite from the archive, but it appears this is
a [paid feature](https://sqlite.org/purchase/zipvfs).
### Download the Latest Version
You can modify the previous code slightly by omitting the `version` keyword
argument to automatically find the latest version of ChEMBL:
```python
import chembl_downloader
path = chembl_downloader.download_extract_sqlite()
```
The `version` keyword argument is available for all functions in this package (e.g., including
`connect()`, `cursor()`, and `query()`), but will be omitted below for brevity.
### Automate Connection
Inside the archive is a single SQLite database file. Normally, people manually untar this folder then do something with
the resulting file. Don't do this, it's not reproducible!
Instead, the file can be downloaded and a connection can be opened automatically with:
```python
import chembl_downloader
with chembl_downloader.connect() as conn:
with conn.cursor() as cursor:
cursor.execute(...) # run your query string
rows = cursor.fetchall() # get your results
```
The `cursor()` function provides a convenient wrapper around this operation:
```python
import chembl_downloader
with chembl_downloader.cursor() as cursor:
cursor.execute(...) # run your query string
rows = cursor.fetchall() # get your results
```
### Run a query and get a pandas DataFrame
The most powerful function is `query()` which builds on the previous `connect()` function in combination
with [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html)
to make a query and load the results into a pandas DataFrame for any downstream use.
```python
import chembl_downloader
sql = """
SELECT
MOLECULE_DICTIONARY.chembl_id,
MOLECULE_DICTIONARY.pref_name
FROM MOLECULE_DICTIONARY
JOIN COMPOUND_STRUCTURES ON MOLECULE_DICTIONARY.molregno == COMPOUND_STRUCTURES.molregno
WHERE molecule_dictionary.pref_name IS NOT NULL
LIMIT 5
"""
df = chembl_downloader.query(sql)
df.to_csv(..., sep='\t', index=False)
```
Suggestion 1: use `pystow` to make a reproducible file path that's portable to other people's machines
(e.g., it doesn't have your username in the path).
Suggestion 2: RDKit is now pip-installable with `pip install rdkit-pypi`, which means most users don't have to muck
around with complicated conda environments and configurations. One of the powerful but understated tools in RDKit is
the [rdkit.Chem.PandasTools](https://rdkit.org/docs/source/rdkit.Chem.PandasTools.html)
module.
## SDF Usage
### Access an RDKit supplier over entries in the SDF dump
This example is a bit more fit-for-purpose than the last two. The `supplier()` function makes sure that the latest SDF
dump is downloaded and loads it from the gzip file into a `rdkit.Chem.ForwardSDMolSupplier`
using a context manager to make sure the file doesn't get closed until after parsing is done. Like the previous
examples, it can also explicitly take a `version`.
```python
from rdkit import Chem
import chembl_downloader
with chembl_downloader.supplier() as suppl:
data = []
for i, mol in enumerate(suppl):
if mol is None or mol.GetNumAtoms() > 50:
continue
fp = Chem.PatternFingerprint(mol, fpSize=1024, tautomerFingerprints=True)
smi = Chem.MolToSmiles(mol)
data.append((smi, fp))
```
This example was adapted from Greg Landrum's RDKit blog post
on [generalized substructure search](https://greglandrum.github.io/rdkit-blog/tutorial/substructure/2021/08/03/generalized-substructure-search.html).
### Iterate over SMILES
This example uses the `supplier()` method and RDKit to get SMILES strings from molecules
in ChEMBL's SDF file. If you want direct access to the RDKit molecule objects, use `supplier()`.
```python
import chembl_downloader
for smiles in chembl_downloader.iterate_smiles():
print(smiles)
```
### Get an RDKit substructure library
Building on the `supplier()` function, the `get_substructure_library()`
makes the preparation of a [substructure library](https://www.rdkit.org/docs/cppapi/classRDKit_1_1SubstructLibrary.html)
automated and reproducible. Additionally, it caches the results of the build,
which takes on the order of tens of minutes, only has to be done once and future
loading from a pickle object takes on the order of seconds.
The implementation was inspired by Greg Landrum's RDKit blog post,
[Some new features in the SubstructLibrary](https://greglandrum.github.io/rdkit-blog/tutorial/substructure/2021/12/20/substructlibrary-search-order.html).
The following example shows how it can be used to accomplish some of the first
tasks presented in the post:
```python
from rdkit import Chem
import chembl_downloader
library = chembl_downloader.get_substructure_library()
query = Chem.MolFromSmarts('[O,N]=C-c:1:c:c:n:c:c:1')
matches = library.GetMatches(query)
```
## Morgan Fingerprints Usage
### Get the Morgan Fingerprint file
ChEMBL makes a file containing pre-computed 2048 bit radius 2 morgan
fingerprints for each molecule available. It can be downloaded using:
```python
import chembl_downloader
path = chembl_downloader.download_fps()
```
The `version` and other keyword arguments are also valid for this function.
### Load fingerprints with [`chemfp`](https://chemfp.com/)
The following wraps the `download_fps` function with `chemfp`'s fingerprint
loader:
```python
import chembl_downloader
arena = chembl_downloader.chemfp_load_fps()
```
The `version` and other keyword arguments are also valid for this function.
More information on working with the `arena` object can be found
[here](https://chemfp.readthedocs.io/en/latest/using-api.html#working-with-a-fingerprintarena).
## Extras
### Store in a Different Place
If you want to store the data elsewhere using `pystow` (e.g., in [`pyobo`](https://github.com/pyobo/pyobo)
I also keep a copy of this file), you can use the `prefix` argument.
```python
import chembl_downloader
# It gets downloaded/extracted to
# ~/.data/pyobo/raw/chembl/29/chembl_29/chembl_29_sqlite/chembl_29.db
path = chembl_downloader.download_extract_sqlite(prefix=['pyobo', 'raw', 'chembl'])
```
See the `pystow` [documentation](https://github.com/cthoyt/pystow#%EF%B8%8F-configuration) on configuring the storage
location further.
The `prefix` keyword argument is available for all functions in this package (e.g., including
`connect()`, `cursor()`, and `query()`).
### Download via CLI
After installing, run the following CLI command to ensure it and send the path to stdout
```bash
$ chembl_downloader
```
Use `--test` to show two example queries
```bash
$ chembl_downloader --test
```
## Contributing
Please read the contribution guidelines in [CONTRIBUTING.md](.github/CONTRIBUTING.md).
If you'd like to contribute, there's a submodule called `chembl_downloader.queries`
where you can add a useful SQL queries along with a description of what it does for easy
importing and reuse.
## Users
See [who's using `chembl-downloader`](https://github.com/search?q=chembl_downloader+-user%3Acthoyt&type=Code).
## Statistics and Compatibility
`chembl-downloader` is compatible with all versions of ChEMBL. However, some files are
not available for all versions. For example, the SQLite version of the database was first
added in release 21 (2015-02-12).
| ChEMBL Version | Release Date | Total Named Compounds *from SQLite* |
|------------------|----------------|------------------------------------:|
| 31 | 2022-07-12 | 41,585 |
| 30 | 2022-02-22 | 41,549 |
| 29 | 2021-07-01 | 41,383 |
| 28 | 2021-01-15 | 41,049 |
| 27 | 2020-05-18 | 40,834 |
| 26 | 2020-02-14 | 40,822 |
| 25 | 2019-02-01 | 39,885 |
| 24_1 | 2018-05-01 | 39,877 |
| 24 | | |
| 23 | 2017-05-18 | 39,584 |
| 22_1 | 2016-11-17 | |
| 22 | | 39,422 |
| 21 | 2015-02-12 | 39,347 |
| 20 | 2015-02-03 | - |
| 19 | 2014-07-2333 | - |
| 18 | 2014-04-02 | - |
| 17 | 2013-09-16 | - |
| 16 | 2013-055555-15 | - |
| 15 | 2013-01-30 | - |
| 14 | 2012 -07-18 | - |
| 13 | 2012-02-29 | - |
| 12 | 2011-11-30 | - |
| 11 | 2011-06-07 | - |
| 10 | 2011-06-07 | - |
| 09 | 2011-01-04 | - |
| 08 | 2010-11-05 | - |
| 07 | 2010-09-03 | - |
| 06 | 2010-09-03 | - |
| 05 | 2010-06-07 | - |
| 04 | 2010-05-26 | - |
| 03 | 2010-04-30 | - |
| 02 | 2009-12-07 | - |
| 01 | 2009-10-28 | - |
Raw data
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"description": "<h1 align=\"center\">\n chembl_downloader\n</h1>\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/chembl_downloader\">\n <img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/chembl_downloader\" />\n </a>\n <a href=\"https://pypi.org/project/chembl_downloader\">\n <img alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/chembl_downloader\" />\n </a>\n <a href=\"https://github.com/cthoyt/chembl_downloader/blob/main/LICENSE\">\n <img alt=\"PyPI - License\" src=\"https://img.shields.io/pypi/l/chembl_downloader\" />\n </a>\n <a href=\"https://zenodo.org/badge/latestdoi/390113187\">\n <img src=\"https://zenodo.org/badge/390113187.svg\" alt=\"DOI\" />\n </a>\n <a href=\"https://github.com/psf/black\">\n <img src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" alt=\"Code style: black\" />\n </a>\n <a href='https://chembl-downloader.readthedocs.io/en/latest/?badge=latest'>\n <img src='https://readthedocs.org/projects/chembl-downloader/badge/?version=latest' alt='Documentation Status' />\n </a>\n</p>\n\nDon't worry about downloading/extracting ChEMBL or versioning - just use ``chembl_downloader`` to write code that knows\nhow to download it and use it automatically.\n\nInstall with:\n\n```bash\n$ pip install chembl-downloader\n```\n\nFull technical documentation can be found on\n[ReadTheDocs](https://chembl-downloader.readthedocs.io). Tutorials can be found\nin Jupyter notebooks in the [notebooks/](notebooks/) directory of the\nrepository.\n\n## Database Usage\n\n### Download A Specific Version\n\n```python\nimport chembl_downloader\n\npath = chembl_downloader.download_extract_sqlite(version='28')\n```\n\nAfter it's been downloaded and extracted once, it's smart and does not need to download again. It gets stored\nusing [`pystow`](https://github.com/cthoyt/pystow) automatically in the `~/.data/chembl`\ndirectory.\n\nWe'd like to implement something such that it could load directly into SQLite from the archive, but it appears this is\na [paid feature](https://sqlite.org/purchase/zipvfs).\n\n### Download the Latest Version\n\nYou can modify the previous code slightly by omitting the `version` keyword\nargument to automatically find the latest version of ChEMBL:\n\n```python\nimport chembl_downloader\n\npath = chembl_downloader.download_extract_sqlite()\n```\n\nThe `version` keyword argument is available for all functions in this package (e.g., including\n`connect()`, `cursor()`, and `query()`), but will be omitted below for brevity.\n\n### Automate Connection\n\nInside the archive is a single SQLite database file. Normally, people manually untar this folder then do something with\nthe resulting file. Don't do this, it's not reproducible!\nInstead, the file can be downloaded and a connection can be opened automatically with:\n\n```python\nimport chembl_downloader\n\nwith chembl_downloader.connect() as conn:\n with conn.cursor() as cursor:\n cursor.execute(...) # run your query string\n rows = cursor.fetchall() # get your results\n```\n\nThe `cursor()` function provides a convenient wrapper around this operation:\n\n```python\nimport chembl_downloader\n\nwith chembl_downloader.cursor() as cursor:\n cursor.execute(...) # run your query string\n rows = cursor.fetchall() # get your results\n```\n\n### Run a query and get a pandas DataFrame\n\nThe most powerful function is `query()` which builds on the previous `connect()` function in combination\nwith [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html)\nto make a query and load the results into a pandas DataFrame for any downstream use.\n\n```python\nimport chembl_downloader\n\nsql = \"\"\"\nSELECT\n MOLECULE_DICTIONARY.chembl_id,\n MOLECULE_DICTIONARY.pref_name\nFROM MOLECULE_DICTIONARY\nJOIN COMPOUND_STRUCTURES ON MOLECULE_DICTIONARY.molregno == COMPOUND_STRUCTURES.molregno\nWHERE molecule_dictionary.pref_name IS NOT NULL\nLIMIT 5\n\"\"\"\n\ndf = chembl_downloader.query(sql)\ndf.to_csv(..., sep='\\t', index=False)\n```\n\nSuggestion 1: use `pystow` to make a reproducible file path that's portable to other people's machines\n(e.g., it doesn't have your username in the path).\n\nSuggestion 2: RDKit is now pip-installable with `pip install rdkit-pypi`, which means most users don't have to muck\naround with complicated conda environments and configurations. One of the powerful but understated tools in RDKit is\nthe [rdkit.Chem.PandasTools](https://rdkit.org/docs/source/rdkit.Chem.PandasTools.html)\nmodule.\n\n## SDF Usage\n\n### Access an RDKit supplier over entries in the SDF dump\n\nThis example is a bit more fit-for-purpose than the last two. The `supplier()` function makes sure that the latest SDF\ndump is downloaded and loads it from the gzip file into a `rdkit.Chem.ForwardSDMolSupplier`\nusing a context manager to make sure the file doesn't get closed until after parsing is done. Like the previous\nexamples, it can also explicitly take a `version`.\n\n```python\nfrom rdkit import Chem\n\nimport chembl_downloader\n\nwith chembl_downloader.supplier() as suppl:\n data = []\n for i, mol in enumerate(suppl):\n if mol is None or mol.GetNumAtoms() > 50:\n continue\n fp = Chem.PatternFingerprint(mol, fpSize=1024, tautomerFingerprints=True)\n smi = Chem.MolToSmiles(mol)\n data.append((smi, fp))\n```\n\nThis example was adapted from Greg Landrum's RDKit blog post\non [generalized substructure search](https://greglandrum.github.io/rdkit-blog/tutorial/substructure/2021/08/03/generalized-substructure-search.html).\n\n### Iterate over SMILES\n\nThis example uses the `supplier()` method and RDKit to get SMILES strings from molecules \nin ChEMBL's SDF file. If you want direct access to the RDKit molecule objects, use `supplier()`.\n\n```python\nimport chembl_downloader\n\nfor smiles in chembl_downloader.iterate_smiles():\n print(smiles)\n```\n\n### Get an RDKit substructure library\n\nBuilding on the `supplier()` function, the `get_substructure_library()`\nmakes the preparation of a [substructure library](https://www.rdkit.org/docs/cppapi/classRDKit_1_1SubstructLibrary.html)\nautomated and reproducible. Additionally, it caches the results of the build,\nwhich takes on the order of tens of minutes, only has to be done once and future\nloading from a pickle object takes on the order of seconds.\n\nThe implementation was inspired by Greg Landrum's RDKit blog post,\n[Some new features in the SubstructLibrary](https://greglandrum.github.io/rdkit-blog/tutorial/substructure/2021/12/20/substructlibrary-search-order.html).\nThe following example shows how it can be used to accomplish some of the first\ntasks presented in the post:\n\n```python\nfrom rdkit import Chem\n\nimport chembl_downloader\n\nlibrary = chembl_downloader.get_substructure_library()\nquery = Chem.MolFromSmarts('[O,N]=C-c:1:c:c:n:c:c:1')\nmatches = library.GetMatches(query)\n```\n\n## Morgan Fingerprints Usage\n\n### Get the Morgan Fingerprint file\n\nChEMBL makes a file containing pre-computed 2048 bit radius 2 morgan\nfingerprints for each molecule available. It can be downloaded using:\n\n```python\nimport chembl_downloader\n\npath = chembl_downloader.download_fps()\n```\n\nThe `version` and other keyword arguments are also valid for this function.\n\n### Load fingerprints with [`chemfp`](https://chemfp.com/)\n\nThe following wraps the `download_fps` function with `chemfp`'s fingerprint\nloader:\n\n```python\nimport chembl_downloader\n\narena = chembl_downloader.chemfp_load_fps()\n```\n\nThe `version` and other keyword arguments are also valid for this function.\nMore information on working with the `arena` object can be found\n[here](https://chemfp.readthedocs.io/en/latest/using-api.html#working-with-a-fingerprintarena).\n\n## Extras\n\n### Store in a Different Place\n\nIf you want to store the data elsewhere using `pystow` (e.g., in [`pyobo`](https://github.com/pyobo/pyobo)\nI also keep a copy of this file), you can use the `prefix` argument.\n\n```python\nimport chembl_downloader\n\n# It gets downloaded/extracted to \n# ~/.data/pyobo/raw/chembl/29/chembl_29/chembl_29_sqlite/chembl_29.db\npath = chembl_downloader.download_extract_sqlite(prefix=['pyobo', 'raw', 'chembl'])\n```\n\nSee the `pystow` [documentation](https://github.com/cthoyt/pystow#%EF%B8%8F-configuration) on configuring the storage\nlocation further.\n\nThe `prefix` keyword argument is available for all functions in this package (e.g., including\n`connect()`, `cursor()`, and `query()`).\n\n### Download via CLI\n\nAfter installing, run the following CLI command to ensure it and send the path to stdout\n\n```bash\n$ chembl_downloader\n```\n\nUse `--test` to show two example queries\n\n```bash\n$ chembl_downloader --test\n```\n\n## Contributing\n\nPlease read the contribution guidelines in [CONTRIBUTING.md](.github/CONTRIBUTING.md).\n\nIf you'd like to contribute, there's a submodule called `chembl_downloader.queries`\nwhere you can add a useful SQL queries along with a description of what it does for easy\nimporting and reuse.\n\n## Users\n\nSee [who's using `chembl-downloader`](https://github.com/search?q=chembl_downloader+-user%3Acthoyt&type=Code).\n\n## Statistics and Compatibility\n\n`chembl-downloader` is compatible with all versions of ChEMBL. However, some files are\nnot available for all versions. For example, the SQLite version of the database was first\nadded in release 21 (2015-02-12).\n\n| ChEMBL Version | Release Date | Total Named Compounds *from SQLite* |\n|------------------|----------------|------------------------------------:|\n| 31 | 2022-07-12 | 41,585 |\n| 30 | 2022-02-22 | 41,549 |\n| 29 | 2021-07-01 | 41,383 |\n| 28 | 2021-01-15 | 41,049 |\n| 27 | 2020-05-18 | 40,834 |\n| 26 | 2020-02-14 | 40,822 |\n| 25 | 2019-02-01 | 39,885 |\n| 24_1 | 2018-05-01 | 39,877 |\n| 24 | | |\n| 23 | 2017-05-18 | 39,584 |\n| 22_1 | 2016-11-17 | |\n| 22 | | 39,422 |\n| 21 | 2015-02-12 | 39,347 |\n| 20 | 2015-02-03 | - |\n| 19 | 2014-07-2333 | - |\n| 18 | 2014-04-02 | - |\n| 17 | 2013-09-16 | - |\n| 16 | 2013-055555-15 | - |\n| 15 | 2013-01-30 | - |\n| 14 | 2012 -07-18 | - |\n| 13 | 2012-02-29 | - |\n| 12 | 2011-11-30 | - |\n| 11 | 2011-06-07 | - |\n| 10 | 2011-06-07 | - |\n| 09 | 2011-01-04 | - |\n| 08 | 2010-11-05 | - |\n| 07 | 2010-09-03 | - |\n| 06 | 2010-09-03 | - |\n| 05 | 2010-06-07 | - |\n| 04 | 2010-05-26 | - |\n| 03 | 2010-04-30 | - |\n| 02 | 2009-12-07 | - |\n| 01 | 2009-10-28 | - |\n",
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