#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This file is part of the `pypath` python module
#
# Copyright 2014-2023
# EMBL, EMBL-EBI, Uniklinik RWTH Aachen, Heidelberg University
#
# Authors: see the file `README.rst`
# Contact: Dénes Türei (turei.denes@gmail.com)
#
# Distributed under the GPLv3 License.
# See accompanying file LICENSE.txt or copy at
# https://www.gnu.org/licenses/gpl-3.0.html
#
# Website: https://pypath.omnipathdb.org/
#
from typing import Optional, Union
import csv
import collections
import pypath.share.curl as curl
import pypath.share.session as session
import pypath.share.common as common
import pypath.resources.urls as urls
import pypath.utils.taxonomy as taxonomy
_logger = session.Logger(name = 'drugcentral_input')
_log = _logger._log
[docs]
def drugcentral_drugs() -> list[tuple]:
"""
Drug names and structures from Drug Central.
Returns
List of drugs, each represented by a named tuple.
"""
DrugcentralDrug = collections.namedtuple(
'DrugcentralDrug',
(
'drugcentral',
'inn',
'cas',
'smiles',
'inchikey',
'inchi',
)
)
url = urls.urls['drugcentral']['SMILES_InChI']
c = curl.Curl(url, large = True, silent = False)
drugs = list(csv.DictReader(c.result, delimiter = '\t'))
result = [
DrugcentralDrug(
drugcentral = drug['ID'],
inn = drug['INN'],
cas = drug['CAS_RN'],
smiles = drug['SMILES'],
inchikey = drug['InChIKey'],
inchi = drug['InChI'],
)
for drug in drugs
]
return result
[docs]
def drugcentral_interactions(
organism: Optional[Union[str, int]] = None,
comments: bool = False,
) -> list[tuple]:
"""
Retrieves drug-target interactions from Drug Central.
Args
organism:
Organism name or NCBI Taxonomy ID. If not provided,
all organisms will be retained.
comments:
Include comments in the result.
Returns
List of drug-target relationships, represented as named tuples.
"""
DrugcentralInteraction = collections.namedtuple(
'DrugcentralInteraction',
(
'drug',
'drug_name',
'uniprot',
'target_type',
'canonical',
'act_value',
'act_type',
'relation',
'effect',
'tdl',
'organism',
'comment',
),
)
url = urls.urls['drugcentral']['interactions']
c = curl.Curl(url, large = True, silent = False)
interactions = list(csv.DictReader(c.result, delimiter = '\t'))
organism_latin = taxonomy.ensure_latin_name(organism)
if organism and not organism_latin:
msg = f'Could not find latin name for organism: `{organism}`.'
_log(msg)
drugs = dict(
(d.drugcentral, d)
for d in drugcentral_drugs()
)
result = [
DrugcentralInteraction(
drug = drugs.get(i['STRUCT_ID'], None),
drug_name = i['DRUG_NAME'],
uniprot = uniprot,
target_type = i['TARGET_CLASS'],
canonical = i['MOA'] == '1',
act_value = common.try_float(i['ACT_VALUE']) or None,
act_type = i['ACT_TYPE'],
relation = i['RELATION'] or None, # what is relation??
effect = i['ACTION_TYPE'] or None,
tdl = i['TDL'],
organism = i['ORGANISM'],
comment = i['ACT_COMMENT'] if comments else None,
)
for i in interactions
for uniprot in i['ACCESSION'].split('|')
if not organism_latin or i['ORGANISM'] == organism_latin
]
return result
[docs]
def drugcentral_mapping(
id_type: str,
target_id_type: str,
) -> dict[str, set[str]]:
"""
Identifier translation table from Drug Central.
Available ID types: drugcentral, inn, cas, smiles, inchikey, inchi.
Args
id_type:
The identifier type to be used as keys.
target_id_type:
The identifier type that will be collected into the values.
Returns
An identifier translation table.
"""
drugs = drugcentral_drugs()
result = collections.defaultdict(set)
for d in drugs:
the_id = getattr(d, id_type)
target_id = getattr(d, target_id_type)
if the_id and target_id:
result[the_id].add(target_id)
return dict(result)
