Download, open, and query ChEMBL through SQLite
Project description
chembl_downloader
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.
Installation
$ pip install chembl-downloader
Usage
Download A Specific Version
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
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.
Download the Latest Version
First, you'll have to install bioversions
with pip install bioversions
, whose job it is to look up the latest version of many databases. Then, you can modify
the previous code slightly by omitting the version
keyword argument:
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:
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:
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
to make a query and load the results into a pandas DataFrame for any downstream use.
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
module.
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
.
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.
Store in a Different Place
If you want to store the data elsewhere using pystow
(e.g., in pyobo
I also keep a copy of this file), you can use the prefix
argument.
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 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
$ chembl_downloader
Use --test
to show two example queries
$ chembl_downloader --test
Contributing
If you'd like to contribute, there's a submodule called chembl_downloader.queries
where you can add an SQL query along with a description of what it does for easy importing.
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