def fetch_all_by_trgm_sim(self, smiles, *expr, **kwargs):
"""
Returns all fragments that are similar to the given SMILES string
using trigam similarity (similar to LINGO).
Parameters
----------
threshold : float, default=0.6
Similarity threshold that will be used for searching.
limit : int, default=25
Maximum number of hits that will be returned.
Returns
-------
resultset : list
List of tuples (Fragment, similarity) containing the chemical components
and the calculated trigram similarity.
Queried Entities
----------------
Fragment
Examples
--------
>>>> FragmentAdaptor().fetch_all_by_trgm_sim('Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CC[NH+](CC5)C')
[(<Fragment(STI)>, 0.883721), (<Fragment(NIL)>, 0.73913),
(<Fragment(PRC)>, 0.738095), (<Fragment(406)>, 0.666667),
(<Fragment(J07)>, 0.604167), (<Fragment(AD5)>, 0.6),
(<Fragment(AAX)>, 0.6), (<Fragment(VX6)>, 0.6)]
Requires
--------
.. important:: `pg_trgm <http://www.postgresql.org/docs/current/static/pgtrgm.html>`_ PostgreSQL extension.
"""
session = Session()
threshold = kwargs.get('threshold', 0.6)
# SET THE SIMILARITY THRESHOLD FOR THE INDEX
session.execute(
text("SELECT set_limit(:threshold)").execution_options(
autocommit=True).params(threshold=threshold))
similarity = func.similarity(Fragment.ism, smiles).label('similarity')
sim_thresh = func.show_limit().label('sim_thresh')
query = self.query.add_columns(similarity, sim_thresh)
query = query.filter(and_(Fragment.like(smiles), *expr))
# KNN-GIST
query = query.order_by(Fragment.ism.op('<->')(smiles))
if kwargs.get('limit'):
query = query.limit(kwargs['limit'])
results = query.all()
#session.close()
return results #query
def abstracts(self):
"""
Returns the abstract(s) of the journal articles that are associated with
this PDB entry.
"""
session = Session()
statement = select([citations],
and_(citations.c.pubmed_id==cast(XRef.xref, Integer),
XRef.source=='PubMed', XRef.entity_type=='Structure',
XRef.entity_id==self.structure_id))
return session.execute(statement).fetchall()
def disordered_regions(self, *expr):
"""
Returns a list of disordered regions inside this Chain (if any).
"""
session = Session()
statement = select([disordered_regions],
and_(disordered_regions.c.pdb==self.Biomolecule.Structure.pdb,
disordered_regions.c.pdb_chain_id==self.pdb_chain_asu_id,
*expr))
result = session.execute(statement).fetchall()
return result
def fetch_all_by_sim_oe(self, smiles, *expr, **kwargs):
"""
Returns all Chemical Components that match the given SMILES string with at
least the given similarity threshold using chemical fingerprints.
Parameters
----------
smi : str
The query rdmol in SMILES format.
threshold : float, default=0.5
The similarity threshold that will be used for searching.
fp : {'circular','atompair','torsion'}
RDKit fingerprint type to be used for similarity searching.
*expr : BinaryExpressions, optional
SQLAlchemy BinaryExpressions that will be used to filter the query.
Queried Entities
----------------
ChemComp, ChemCompOEFP
Returns
-------
hits : list
List of tuples in the form (ChemComp, similarity)
Examples
--------
Requires
--------
.. important:: OpenEye cartridge.
"""
session = Session()
threshold = kwargs.get('threshold')
metric = kwargs.get('metric', 'tanimoto')
fp = kwargs.get('fp', 'circular')
limit = kwargs.get('limit', 100)
# set the similarity threshold for the selected metric
if threshold:
statement = text(
"SELECT openeye.set_oefp_similarity_limit(:threshold, :metric)"
)
session.execute(
statement.params(threshold=threshold, metric=metric))
if fp == 'circular':
query = func.openeye.make_circular_fp(smiles)
target = ChemCompOEFP.circular_fp
elif fp == 'maccs166':
query = func.openeye.make_maccs166_fp(smiles)
target = ChemCompOEFP.maccs166_fp
elif fp == 'path':
query = func.openeye.make_path_fp(smiles)
target = ChemCompOEFP.path_fp
elif fp == 'tree':
query = func.openeye.make_tree_fp(smiles)
target = ChemCompOEFP.tree_fp
else:
raise ValueError(
"cannot create fingerprint: type {0} does not exist.".format(
fp))
# compile similarity metric and the correspoding GIST index / KNN-GIST
if metric == 'tanimoto':
similarity = func.openeye.tanimoto(query, target)
index = func.openeye.tanimoto_is_above_limit(target, query)
orderby = target.op('OPERATOR(openeye.<%%>)')(query) # escape %
elif metric == 'dice':
similarity = func.openeye.dice(query, target)
index = func.openeye.dice_is_above_limit(target, query)
orderby = target.op('OPERATOR(openeye.<#>)')(query)
elif metric == 'manhattan':
similarity = func.openeye.manhattan(query, target)
index = func.openeye.manhattan_is_above_limit(target, query)
orderby = target.op('OPERATOR(openeye.<~>)')(query)
elif metric == 'cosine':
similarity = func.openeye.cosine(query, target)
index = func.openeye.cosine_is_above_limit(target, query)
orderby = target.op('OPERATOR(openeye.<@>)')(query)
elif metric == 'euclidean':
similarity = func.openeye.euclidean(query, target)
index = func.openeye.euclidean_is_above_limit(target, query)
orderby = target.op('OPERATOR(openeye.<->)')(query)
else:
raise ValueError(
"{} is not a valid similarity metric.".format(metric))
query = ChemComp.query.add_column(similarity)
query = query.join('OEFP').filter(and_(index, *expr))
query = query.order_by(orderby)
return query
def fetch_all_by_sim(self, smi, *expr, **kwargs):
"""
Returns all fragments that match the given SMILES string with at
least the given similarity threshold using chemical fingerprints.
Parameters
----------
smi : str
The query rdmol in SMILES format.
threshold : float, default=0.5
The similarity threshold that will be used for searching.
fp : {'circular','atompair','torsion','maccs','layered','avalon'}
RDKit fingerprint type to be used for similarity searching.
*expr : BinaryExpressions, optional
SQLAlchemy BinaryExpressions that will be used to filter the query.
Queried Entities
----------------
Fragments, FragmentRDFP
Returns
-------
hits : list
List of tuples in the form (Fragment, similarity)
Examples
--------
>>> #PENDING
Requires
--------
.. important:: `RDKit <http://www.rdkit.org>`_ PostgreSQL cartridge.
"""
session = Session()
threshold = kwargs.get('threshold', 0.5)
metric = kwargs.get('metric', 'tanimoto')
fp = kwargs.get('fp', 'circular')
if fp == 'circular':
query = func.rdkit.morganbv_fp(smi, 2).label('queryfp')
target = FragmentRDFP.circular_fp
elif fp == 'torsion':
query = func.rdkit.torsionbv_fp(smi).label('queryfp')
target = FragmentRDFP.torsion_fp
elif fp == 'atompair':
query = func.rdkit.atompairbv_fp(smi).label('queryfp')
target = FragmentRDFP.atompair_fp
elif fp == 'maccs':
query = func.rdkit.maccs_fp(smi).label('queryfp')
target = FragmentRDFP.maccs_fp
elif fp == 'layered':
query = func.rdkit.layered_fp(smi).label('queryfp')
target = FragmentRDFP.layered_fp
elif fp == 'avalon':
query = func.rdkit.avalon_fp(smi).label('queryfp')
target = FragmentRDFP.avalon_fp
else:
msg = "The fingerprint type [{0}] does not exist.".format(fp)
raise RuntimeError(msg)
# set the similarity threshold for the index
if metric == 'tanimoto':
session.execute(
text("SET rdkit.tanimoto_threshold=:threshold").
execution_options(autocommit=True).params(threshold=threshold))
sim_thresh = func.current_setting(
'rdkit.tanimoto_threshold').label('sim_thresh')
similarity = func.rdkit.tanimoto_sml(query,
target).label('similarity')
index = func.rdkit.tanimoto_sml_op(query, target)
elif metric == 'dice':
session.execute(
text("SET rdkit.dice_threshold=:threshold").execution_options(
autocommit=True).params(threshold=threshold))
sim_thresh = func.current_setting('rdkit.dice_threshold').label(
'sim_thresh')
similarity = func.rdkit.dice_sml(query, target).label('similarity')
index = func.rdkit.dice_sml_op(query, target)
query = self.query.add_columns(similarity, sim_thresh)
query = query.join('RDFP').filter(and_(index, *expr))
query = query.order_by('similarity DESC')
if kwargs.get('limit'):
query = query.limit(kwargs['limit']) #.all(
#print query.statement
results = query.all()
#session.close()
return results # query