def __main__():
"""
Perform substructure searches on the selected db based on Fingerprint descriptors.
"""
args = parse_command_line(sys.argv)
db_conn = cheminfolib.db_connect(args)
if args.istyle == "pattern":
mol = pybel.readstring("smi", args.input)
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(create_query(args, mol))
rows = cur.fetchall()
else:
old_rows = []
for mol in pybel.readfile("smi", args.input):
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(create_query(args, mol))
old_rows.append(cur.fetchall())
# TODO: if i have a testing env, try to not append the cur.fetchall ... + it and save the one loop here
rows = []
for first_row in old_rows:
for row in first_row:
rows.append(row)
cheminfolib.print_output(args, rows)
def __main__():
"""
Query the database and retrieve the requested compounds. A unique identifier for the molecules must be provided in a tab file
"""
args = parse_command_line(sys.argv)
identifiers = [
line.split("\t")[args.column - 1].strip() for line in open(args.input, "r")
]
db_conn = cheminfolib.db_connect(args)
query = """SELECT *
FROM %(libname)s.tbl_molecule molec
LEFT JOIN %(libname)s.tbl_molecule_synonym syn
ON molec.id = syn.fk_molecule_id
WHERE syn.synonym IN ('%(id)s')
""" % {
"libname": args.lib,
"id": ("', '").join(identifiers),
}
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(query)
rows = cur.fetchall()
cheminfolib.print_output(args, rows)
def __main__():
"""
Filter a db using user-selected physico-chemical properties
"""
args = parse_command_line(sys.argv)
db_conn = cheminfolib.db_connect(args)
filters = json.loads((args.filters).replace(" ", "").replace(",}", "}"))
query = """SELECT can_smiles, synonym
FROM %(libname)s.tbl_molecule molec
LEFT JOIN (SELECT fk_molecule_id, MAX(synonym) as synonym FROM %(libname)s.tbl_molecule_synonym GROUP BY fk_molecule_id) syn
ON molec.id = syn.fk_molecule_id
WHERE %(filter_query)s
""" % {
"libname": args.lib,
"filter_query": " AND ".join(
[("%s BETWEEN %s AND %s" % (key, elem[0], elem[1])) for key, elem in filters.items()]
),
}
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(query)
rows = cur.fetchall()
# Only fetch smiles from the database since SDF retrieval takes ages
args.oformat = "can_smiles"
cheminfolib.print_output(args, rows)
def __main__():
"""
Filter a db using user-selected physico-chemical properties
"""
args = parse_command_line(sys.argv)
db_conn = cheminfolib.db_connect(args)
filters = json.loads((args.filters).replace(" ", "").replace(",}", "}"))
query = """SELECT can_smiles, synonym
FROM %(libname)s.tbl_molecule molec
LEFT JOIN (SELECT fk_molecule_id, MAX(synonym) as synonym FROM %(libname)s.tbl_molecule_synonym GROUP BY fk_molecule_id) syn
ON molec.id = syn.fk_molecule_id
WHERE %(filter_query)s
""" % {
"libname":
args.lib,
"filter_query":
" AND ".join([("%s BETWEEN %s AND %s" % (key, elem[0], elem[1]))
for key, elem in filters.items()]),
}
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(query)
rows = cur.fetchall()
# Only fetch smiles from the database since SDF retrieval takes ages
args.oformat = "can_smiles"
cheminfolib.print_output(args, rows)
def __main__():
"""
Perform substructure searches on the selected db based on Fingerprint descriptors.
"""
args = parse_command_line(sys.argv)
db_conn = cheminfolib.db_connect(args)
if args.istyle == "pattern":
mol = pybel.readstring("smi", args.input)
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(create_query(args, mol))
rows = cur.fetchall()
else:
old_rows = []
for mol in pybel.readfile("smi", args.input):
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(create_query(args, mol))
old_rows.append(cur.fetchall())
### TODO: if i have a testing env, try to not append the cur.fetchall ... + it and save the one loop here
rows = []
for first_row in old_rows:
for row in first_row:
rows.append(row)
cheminfolib.print_output(args, rows)
def __main__():
"""
Query the database and retrieve the requested compounds. A unique identifier for the molecules must be provided in a tab file
"""
args = parse_command_line(sys.argv)
identifiers = [ line.split('\t')[args.column - 1].strip() for line in open(args.input, 'r') ]
db_conn = cheminfolib.db_connect(args)
query = ("""SELECT *
FROM %(libname)s.tbl_molecule molec
LEFT JOIN %(libname)s.tbl_molecule_synonym syn
ON molec.id = syn.fk_molecule_id
WHERE syn.synonym IN ('%(id)s')
"""
% { 'libname' : args.lib, 'id' : ("', '").join(identifiers) } )
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(query)
rows = cur.fetchall()
cheminfolib.print_output(args, rows)
def __main__():
"""
Perform similarity searches on the selected db based on Tanimoto similarity of Fingerprint descriptors.
"""
args = parse_command_line(sys.argv)
db_conn = cheminfolib.db_connect(args)
if args.istyle == "pattern":
mol = pybel.readstring("smi", args.input)
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(create_query(args, mol))
rows = cur.fetchall()
else:
old_rows = []
for mol in pybel.readfile("smi", args.input):
cur = db_conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(create_query(args, mol))
old_rows.append(cur.fetchall())
rows = []
# this crap deletes one level from the list...
for first_row in old_rows:
for row in first_row:
rows.append(row)
if args.oformat in ["table", "sdf"]:
# Add the Tanimoto coefficient as output for the tabular and sdf selections in similarity searches
args.fetch += ", %s" % "tani"
cheminfolib.print_output(args, rows)