Example structure queries¶
This tutorial is based on a similar document available from the RDKit wiki and it illustrates how to use Razi to perform substructure and superstructure queries on a chemical database.
No dedicated database is created for this tutorial. The same database used in the “Example similarity queries” tutorial can be used instead. If you are no longer connected to the database, the connection configuration and schema definition must be entered again. If you are still connected just skip to section “Querying the database”.
Connection to the database¶
Start your python interpreter and configure a database connection:
from sqlalchemy import create_engine
engine = create_engine('postgresql://db_user:db_password@host:1234/razi_tutorial')
also, define the database session factory object:
from sqlalchemy.orm import sessionmaker
Session = sessionmaker(bind=engine)
Schema definition¶
Then define the mapping to the database table:
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base(bind=engine)
from sqlalchemy import Column, Integer, String
from razi.orm import ChemColumn
from razi.chemtypes import Molecule, BitFingerprint
class Compound(Base):
__tablename__='compounds'
id = Column(Integer, primary_key=True)
name = Column(String)
structure = ChemColumn(Molecule)
atompair = ChemColumn(BitFingerprint)
torsion = ChemColumn(BitFingerprint)
morgan = ChemColumn(BitFingerprint)
def __init__(self, name, structure):
self.name = name
self.structure = structure
self.atompair = self.structure.atompair_b()
self.torsion = self.structure.torsion_b()
self.morgan = self.structure.morgan_b(2)
def __init__(self, name, structure):
self.name = name
self.structure = structure
def __repr__(self):
return '(%s) < %s >' % (self.name, self.structure)
Querying the database¶
Substructure queries¶
Retrieve the number of molecules containing a triazine:
>>> constraint = Compound.structure.contains('c1ncncn1')
>>> print session.query(Compound).filter(constraint).count()
56
Retrieve the number of molecules containing a coumarin:
>>> constraint = Compound.structure.contains('O=C1OC2=CC=CC=C2C=C1')
>>> print session.query(Compound).filter(constraint).count()
166
Get the first 10 of those:
>>> for c in session.query(Compound).filter(constraint)[:10]: print c
(CHEMBL58793) < OC(=O)CCCCc1cc(=O)oc2c1ccc(O)c2CN1CCCC1 >
(CHEMBL56784) < [Na+].COc1ccc(-c2c3n(c4c(=O)oc5cc(OS([O-])(=O)=O)c(OC)cc5c42)CCc2cc(OC)c(OC)cc2-3)cc1O >
(CHEMBL54909) < COc1cc2ccc(=O)oc2c(O)c1O >
(CHEMBL50150) < COc1ccc(CCn2cc(-c3ccc(OC)c(OC)c3)c3c4c(oc(=O)c23)cc(OC)c(OC)c4)cc1OC >
(CHEMBL50201) < CC(C)CCc1c(O)ccc2c1oc(=O)cc2 >
(CHEMBL59509) < OC(=O)CCCCc1cc(=O)oc2c1ccc(O)c2CNc1ccccc1 >
(CHEMBL57330) < CCCN(C1COc2cccc(OC)c2C1)CCCCNC(=O)c1c2c(oc(=O)c1)c1c3c(c2)CCCN3CCC1 >
(CHEMBL57173) < C/C(CC/C=C(\C)C1=CC(=O)C(C)(C)O1)=C\COc1cc2oc(=O)ccc2cc1 >
(CHEMBL57138) < COc1ccc(-c2c3n(c4c(=O)oc5cc(O)c(OC)cc5c42)CCc2c(OC)c(OC)c(OC)cc2-3)cc1O >
(CHEMBL56918) < C/C(=C\COc1ccc2c(oc(=O)cc2)c1)C1=CC(=O)C(C)(C)O1 >
Including property filters¶
Differently from the original RDKit tutorial, chemical descriptor were not introduced into the current database schema. Filtering based on chemical properties can still be introduced, with the difference that these properties are in this case computed while processing the query:
>>> mw = Compound.structure.mw.label('mw')
>>> logp = Compound.structure.logp.label('logp')
>>> # compounds containing coumarin as substructure, with molecular weight
>>> # not above 200, ordered by ascending estimated logp
>>> subset = session.query(Compound, mw, logp).filter(constraint).filter(mw <= 200).order_by(logp)
>>> for row in subset: print row.Compound.name, row.mw, row.logp
CHEMBL32810 178.143 1.2042
CHEMBL51628 162.144 1.4986
CHEMBL12252 192.17 1.51262
CHEMBL6466 146.145 1.793
CHEMBL49732 176.171 1.8016
CHEMBL12626 176.171 1.80702
CHEMBL12208 176.171 1.80702
CHEMBL12279 160.172 2.10142
CHEMBL12636 190.198 2.11002
CHEMBL19240 190.198 2.11544
CHEMBL53569 186.166 2.5392
CHEMBL6355 196.205 2.9462
Other kinds of structural searches¶
Superstructure queries¶
Look for molecules in the database that are substructures of a query (i.e. where the query is a superstructure of the database molecule):
>>> constraint = Compound.structure.contained_in('c1ccc(C(COC(c2c(=O)oc3c(ccc(O)c3)c2)=O)=O)cc1')
>>> for c in session.query(Compound).filter(constraint)[:10]: print c
(CHEMBL51628) < O=c1oc2cc(O)ccc2cc1 >
(CHEMBL44857) < CCCOC(=O)C >
(CHEMBL44215) < CCOC=O >
(CHEMBL545) < CCO >
(CHEMBL14688) < CO >
(CHEMBL17564) < C >
(CHEMBL15972) < O=Cc1ccccc1 >
(CHEMBL14687) < CCCO >
(CHEMBL16264) < CCOCC >
(CHEMBL14079) < COC(=O)C >
SMARTS-based Queries¶
contains substructure queries are by default executed using SMILES semantics. In order to do SMARTS-based queries, one may use match, as this example shows:
>>> constraint = Compound.structure.match('cc(c)NC(=O)N')
>>> for c in session.query(Compound).filter(constraint)[:10]: print c
(CHEMBL6997) < CSCC[C@H](NC(Nc1cc(C)ccc1)=O)C(=O)N[C@@H](CC(C)C)C(N[C@@H](Cc1ccccc1)C(O)=O)=O >
(CHEMBL6500) < CCOC(c1ccc(NC(=O)Nc2c(C)cc3c(c2)C(C)(C)CC(C)(C)S3)cc1)=O >
(CHEMBL6218) < COc1cc2c(c(N)nc(N3CCN(C(=O)Nc4ccccc4)CC3)n2)cc1OC >
(CHEMBL7610) < COc1ccc(C[C@H](NC(Nc2cc3n(Cc4c(Cl)cccc4Cl)cc(CN4CCCC4)c3cc2)=O)C(N[C@@H](CCCNC(=N)N)C(NCc2ccccc2)=O)=O)cc1 >
(CHEMBL7667) < CCCCNS(=NC(=O)Nc1ccc(Cl)cc1)(=O)c1ccc(C)cc1 >
(CHEMBL7955) < CCNS(=NC(=O)Nc1ccc(Cl)cc1)(=O)c1ccc(C)cc1 >
(CHEMBL7851) < Cc1c(Cl)c(C)cc(S(N)(=NC(=O)Nc2ccc(Cl)cc2)=O)c1 >
(CHEMBL7627) < COc1ccc(C[C@H](NC(Nc2cc3n(Cc4ccc(F)cc4)cc(CNC4CCCC4)c3cc2)=O)C(N[C@@H](CCCN=C(N)N)C(NCc2ccccc2)=O)=O)cc1 >
(CHEMBL7346) < CCOC(c1ccc(NC(=O)Nc2cc3c(cc2)N(C)C(C)(C)C=C3C)cc1)=O >
(CHEMBL7520) < CSCC[C@H](NC(Nc1ccccc1)=O)C(N[C@@H](CC(C)C)C(N[C@@H](Cc1ccccc1)C(O)=O)=O)=O >