The RDKit database cartridge¶
What is this?¶
This document is a tutorial and reference guide for the RDKit PostgreSQL cartridge.
If you find mistakes, or have suggestions for improvements, please either fix them yourselves in the source document (the .rst file) or send them to the mailing list: rdkit-discuss@lists.sourceforge.net (you will need to subscribe first)
Tutorial¶
Introduction¶
Creating databases¶
Configuration¶
The timing information below was collected on a commodity desktop PC (Dell Studio XPS with a 2.9GHz i7 CPU and 8GB of RAM) running Ubuntu 12.04 and using PostgreSQL v9.1.4. The database was installed with default parameters.
To improve performance while loading the database and building the index, I changed a couple of postgres configuration settings in postgresql.conf
fsync = off # turns forced synchronization on or off
synchronous_commit = off # immediate fsync at commit
full_page_writes = off # recover from partial page writes
And to improve search performance, I allowed postgresql to use more memory than the extremely conservative default settings:
shared_buffers = 2048MB # min 128kB
# (change requires restart)
work_mem = 128MB # min 64kB
Creating a database from a file¶
In this example I show how to load a database from the SMILES file of commercially available compounds that is downloadable from emolecules.com at URL http://www.emolecules.com/doc/plus/download-database.php
If you choose to repeat this exact example yourself, please note that it takes several hours to load the 6 million row database and generate all fingerprints.
First create the database and install the cartridge:
~/RDKit_trunk/Data/emolecules > createdb emolecules
~/RDKit_trunk/Data/emolecules > psql -c 'create extension rdkit' emolecules
Now create and populate a table holding the raw data:
~/RDKit_trunk/Data/emolecules > psql -c 'create table raw_data (id SERIAL, smiles text, emol_id integer, parent_id integer)' emolecules
NOTICE: CREATE TABLE will create implicit sequence "raw_data_id_seq" for serial column "raw_data.id"
CREATE TABLE
~/RDKit_trunk/Data/emolecules > zcat emolecules-2013-02-01.smi.gz | sed '1d; s/\\/\\\\/g' | psql -c "copy raw_data (smiles,emol_id,parent_id) from stdin with delimiter ' '" emolecules
Create the molecule table, but only for SMILES that the RDKit accepts:
~/RDKit_trunk/Data/emolecules > psql emolecules
psql (9.1.4)
Type "help" for help.
emolecules=# select * into mols from (select id,mol_from_smiles(smiles::cstring) m from raw_data) tmp where m is not null;
WARNING: could not create molecule from SMILES 'CN(C)C(=[N+](C)C)Cl.F[P-](F)(F)(F)(F)F'
... a lot of warnings deleted ...
SELECT 6008732
emolecules=# create index molidx on mols using gist(m);
CREATE INDEX
The last step is only required if you plan to do substructure searches.
Loading ChEMBL¶
Start by downloading and installing the postgresql dump from the ChEMBL website ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest
Connect to the database, install the cartridge, and create the schema that we’ll use:
chembl_14=# create extension if not exists rdkit;
chembl_14=# create schema rdk;
Create the molecules and build the substructure search index:
chembl_14=# select * into rdk.mols from (select molregno,mol_from_ctab(molfile::cstring) m from compound_structures) tmp where m is not null;
SELECT 1210823
chembl_14=# create index molidx on rdk.mols using gist(m);
CREATE INDEX
chembl_14=# alter table rdk.mols add primary key (molregno);
NOTICE: ALTER TABLE / ADD PRIMARY KEY will create implicit index "mols_pkey" for table "mols"
ALTER TABLE
Create some fingerprints and build the similarity search index:
chembl_14=# select molregno,torsionbv_fp(m) as torsionbv,morganbv_fp(m) as mfp2,featmorganbv_fp(m) as ffp2 into rdk.fps from rdk.mols;
SELECT 1210823
chembl_14=# create index fps_ttbv_idx on rdk.fps using gist(torsionbv);
CREATE INDEX
chembl_14=# create index fps_mfp2_idx on rdk.fps using gist(mfp2);
CREATE INDEX
chembl_14=# create index fps_ffp2_idx on rdk.fps using gist(ffp2);
CREATE INDEX
chembl_14=# alter table rdk.fps add primary key (molregno);
NOTICE: ALTER TABLE / ADD PRIMARY KEY will create implicit index "fps_pkey" for table "fps"
ALTER TABLE
Substructure searches¶
Example query molecules taken from the eMolecules home page:
chembl_14=# select count(*) from rdk.mols where m@>'c1cccc2c1nncc2' ;
count
-------
281
(1 row)
Time: 184.043 ms
chembl_14=# select count(*) from rdk.mols where m@>'c1ccnc2c1nccn2' ;
count
-------
671
(1 row)
Time: 449.998 ms
chembl_14=# select count(*) from rdk.mols where m@>'c1cncc2n1ccn2' ;
count
-------
930
(1 row)
Time: 568.378 ms
chembl_14=# select count(*) from rdk.mols where m@>'Nc1ncnc(N)n1' ;
count
-------
4478
(1 row)
Time: 721.758 ms
chembl_14=# select count(*) from rdk.mols where m@>'c1scnn1' ;
count
-------
10908
(1 row)
Time: 701.036 ms
chembl_14=# select count(*) from rdk.mols where m@>'c1cccc2c1ncs2' ;
count
-------
12823
(1 row)
Time: 1585.473 ms
chembl_14=# select count(*) from rdk.mols where m@>'c1cccc2c1CNCCN2' ;
count
-------
1155
(1 row)
Time: 4567.222 ms
Notice that the last two queries are starting to take a while to execute and count all the results.
Given we’re searching through 1.2 million compounds these search times aren’t incredibly slow, but it would be nice to have them quicker.
One easy way to speed things up, particularly for queries that return a large number of results, is to only retrieve a limited number of results:
chembl_14=# select * from rdk.mols where m@>'c1cccc2c1CNCCN2' limit 100;
molregno | m
----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1292129 | Cc1ccc2c(c1)C(=O)N(N(C)C)CC(=O)N2
1013311 | CCCCC(=O)N1CC(=O)Nc2ccc(F)cc2C1c1ccccc1
1294754 | COc1cc2c(cc1OCc1ccccc1)NC(=O)[C@@H]1CCCN1C2=O
1012025 | O=C(c1cc2ccccc2oc1=O)N1CC(=O)Nc2ccc(Br)cc2C1c1ccc(F)cc1
995226 | CC1Cc2ccccc2N1C(=O)CN1c2ccccc2C(=O)N(C)CC1=O
1291875 | COC(=O)C1=NN2c3ccccc3CN([C@@H](C)c3ccccc3)C(=O)[C@@H]2[C@H]1c1ccccc1
...
1116370 | COc1ccc(CC(=O)N2CC(=O)Nc3ccc(Br)cc3C2c2ccc(F)cc2)cc1OC
1114872 | O=C1[C@@H]2[C@H](C(=O)N1Cc1ccccc1)[C@@H]1C(=O)Nc3ccccc3C(=O)N1[C@@H]2c1ccccc1
Time: 375.747 ms
SMARTS-based queries¶
Oxadiazole or thiadiazole:
chembl_14=# select * from rdk.mols where m@>'c1[o,s]ncn1'::qmol limit 500;
molregno | m
----------+----------------------------------------------------------------------------------------------------------------------------------------------
534296 | Clc1ccccc1CNc1noc(-c2sccc2Br)n1
1178 | CCCCc1oc2ccccc2c1Cc1cccc(/C(C)=C/Cn2oc(=O)[nH]c2=O)c1
566382 | COC(=O)CCc1nc(C2CC(c3ccc(O)c(F)c3)=NO2)no1
499261 | CS/C=C(/C)n1c(=O)onc1C(=O)c1ccc(Br)cc1
450499 | CS(=O)(=O)c1ccc(Nc2ncnc(N3CCC(c4nc(-c5cccc(C(F)(F)F)c5)no4)CC3)c2[N+](=O)[O-])cc1
600176 | Cc1nc(-c2c(Cl)cc(Cl)cc2-c2cnc([C@@H](C)NC(=O)N(C)O)c(F)c2)no1
1213 | CC/C(=C\Cn1oc(=O)[nH]c1=O)c1cccc(OCc2nc(-c3ccc(C(F)(F)F)cc3)oc2C)c1
659277 | Cn1c(N)c(CCCN)c[n+]1CC1=C(C(=O)O)N2C(=O)[C@@H](NC(=O)/C(=N\OC(C)(C)C(=O)O)c3nsc(N)n3)[C@H]2SC1
1316 | CCCCCCCC/C(=C\Cn1oc(=O)[nH]c1=O)c1cccc(OCc2nc(-c3ccc(C(F)(F)F)cc3)oc2C)c1
...
1206 | C/C(Cn1oc(=O)[nH]c1=O)=C(/C)c1cccc(OCc2nc(-c3ccc(C(F)(F)F)cc3)oc2C)c1
1496 | Cc1oc(-c2ccccc2)nc1COc1cccc(C#CC(C)n2oc(=O)[nH]c2=O)c1
Time: 3365.309 ms
This is slower than the pure SMILES query, this is generally true of SMARTS-based queries.
Using Stereochemistry¶
Note that by default stereochemistry is not taken into account when doing substructure queries:
chembl_14=# select * from rdk.mols where m@>'NC(=O)[C@@H]1CCCN1C=O' limit 10;
molregno | m
----------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1295889 | COc1ccc(C[C@@H](C(=O)NCC(N)=O)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CC(C)C)NC(=O)C(C)NC(=O)OCc2ccccc2)cc1
1293815 | CN1C(=O)C23CC4=CC=CC(O)C4N2C(=O)C1(CO)SS3
1293919 | CNC(=O)CNC(=O)C(NC(=O)CNC(=O)C1CCCN1C(=O)C(C)NC(=O)C(NC(=O)OC(C)(C)C)C(C)C)C(C)C
1011887 | COC(=O)C(C)NC(=O)C1CCCN1C(=O)CNC(=O)OCc1ccccc1
1293021 | CCC(C)C1NC(=O)C(NC(=O)C(CC(C)C)N(C)C(=O)[C@@H]2CC(O)CN2C(=O)[C@H](C)O)C(C)OC(=O)[C@H](Cc2ccc(OC)cc2)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CC(C)C)NC(=O)C(C)C(=O)[C@H](C(C)C)OC(=O)CC1O
1287353 | CCC(C)C1NC(=O)C(NC(=O)C(CC(C)C)N(C)C(=O)C2CCCN2C(=O)C(C)O)C(C)OC(=O)C(Cc2ccc(OC)cc2)N(C)C(=O)C2CCCN2C(=O)C(CC(C)C)NC(=O)[C@H](C)C(=O)[C@H](C(C)C)OC(=O)CC1O
1293647 | CCC(C)[C@@H]1NC(=O)[C@@H]2CCCN2C(=O)C(CC(O)CCl)OC(=O)CCNC(=O)[C@H](C)N(C)C(=O)[C@H](C(C)C)N(C)C1=O
1290320 | C=CCOC(=O)[C@@H]1C[C@@H](OC(C)(C)C)CN1C(=O)[C@@H]1[C@H]2OC(C)(C)O[C@H]2CN1C(=O)OCC1c2ccccc2-c2ccccc21
1281392 | COC1=CC2C(=O)N(C)[C@@H](C)C(=O)N3NCCC[C@@H]3C(=O)N3[C@@H](C[C@@]4(O)c5ccc(Cl)cc5N[C@@H]34)C(=O)N[C@H](C(C)C)C(=O)N3NCCC[C@@H]3C(=O)N2N=C1
1014237 | CC(C)COC(=O)N1CC(O)CC1C(=O)Nc1ccc2c(c1)OCO2
(10 rows)
Time: 9.447 ms
This can be changed using the rdkit.do_chiral_sss configuration variable:
chembl_14=# set rdkit.do_chiral_sss=true;
SET
Time: 0.241 ms
chembl_14=# select * from rdk.mols where m@>'NC(=O)[C@@H]1CCCN1C=O' limit 10;
molregno | m
----------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1295889 | COc1ccc(C[C@@H](C(=O)NCC(N)=O)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CC(C)C)NC(=O)C(C)NC(=O)OCc2ccccc2)cc1
1293021 | CCC(C)C1NC(=O)C(NC(=O)C(CC(C)C)N(C)C(=O)[C@@H]2CC(O)CN2C(=O)[C@H](C)O)C(C)OC(=O)[C@H](Cc2ccc(OC)cc2)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CC(C)C)NC(=O)C(C)C(=O)[C@H](C(C)C)OC(=O)CC1O
1293647 | CCC(C)[C@@H]1NC(=O)[C@@H]2CCCN2C(=O)C(CC(O)CCl)OC(=O)CCNC(=O)[C@H](C)N(C)C(=O)[C@H](C(C)C)N(C)C1=O
1290320 | C=CCOC(=O)[C@@H]1C[C@@H](OC(C)(C)C)CN1C(=O)[C@@H]1[C@H]2OC(C)(C)O[C@H]2CN1C(=O)OCC1c2ccccc2-c2ccccc21
1281392 | COC1=CC2C(=O)N(C)[C@@H](C)C(=O)N3NCCC[C@@H]3C(=O)N3[C@@H](C[C@@]4(O)c5ccc(Cl)cc5N[C@@H]34)C(=O)N[C@H](C(C)C)C(=O)N3NCCC[C@@H]3C(=O)N2N=C1
1007418 | C/C=C\C=C\C(=O)N1CC2(CC(c3cccc(NC(=O)/C=C\C=C/C)c3)=NO2)C[C@H]1C(N)=O
785530 | C/C=C/C(=O)N1CC2(CC(c3cccc(NC(=O)CC)c3)=NO2)C[C@H]1C(N)=O
1292152 | CCCCCCCC(=O)N[C@H](C(=O)N[C@H](C(=O)N(C)[C@H](C(=O)N1CCC[C@H]1C(=O)N(C)[C@H](C)C(=O)NCc1ccc(OC)cc1OC)C(C)C)C(C)C)C(C)C
1281390 | CC(C)[C@@H]1NC(=O)[C@@H]2C[C@@]3(O)c4ccc(Cl)cc4N[C@H]3N2C(=O)[C@H]2CCCNN2C(=O)[C@@H](C)N(C)C(=O)[C@H]2CCCNN2C(=O)[C@@H]2CCCNN2C1=O
1057962 | CC[C@H](C)[C@@H]1NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCSC)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@H](Cc2ccccc2)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CCSC)NC(=O)[C@H](CCCCN)NC(=O)[C@@H]2CCCN2C(=O)[C@@H](N)CO)C(C)C)CSSC[C@@H](C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc2cnc[nH]2)C(=O)O)C(C)C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC1=O
(10 rows)
Time: 35.383 ms
Similarity searches¶
Basic similarity searching:
chembl_14=# select count(*) from rdk.fps where mfp2%morganbv_fp('Cc1ccc2nc(-c3ccc(NC(C4N(C(c5cccs5)=O)CCC4)=O)cc3)sc2c1');
count
-------
66
(1 row)
Time: 826.886 ms
Usually we’d like to find a sorted listed of neighbors along with the accompanying SMILES. This SQL function makes that pattern easy:
chembl_14=# create or replace function get_mfp2_neighbors(smiles text)
returns table(molregno integer, m mol, similarity double precision) as
$$
select molregno,m,tanimoto_sml(morganbv_fp(mol_from_smiles($1::cstring)),mfp2) as similarity
from rdk.fps join rdk.mols using (molregno)
where morganbv_fp(mol_from_smiles($1::cstring))%mfp2
order by morganbv_fp(mol_from_smiles($1::cstring))<%>mfp2;
$$ language sql stable ;
CREATE FUNCTION
Time: 0.856 ms
chembl_14=#
chembl_14=# select * from get_mfp2_neighbors('Cc1ccc2nc(-c3ccc(NC(C4N(C(c5cccs5)=O)CCC4)=O)cc3)sc2c1') limit 10;
molregno | m | similarity
----------+-------------------------------------------------------------+-------------------
472512 | Cc1ccc2nc(-c3ccc(NC(=O)C4CCN(C(=O)c5cccs5)CC4)cc3)sc2c1 | 0.772727272727273
471317 | Cc1ccc2nc(-c3ccc(NC(=O)C4CCCN(S(=O)(=O)c5cccs5)C4)cc3)sc2c1 | 0.657534246575342
471461 | Cc1ccc2nc(-c3ccc(NC(=O)C4CCN(S(=O)(=O)c5cccs5)CC4)cc3)sc2c1 | 0.647887323943662
471319 | Cc1ccc2nc(-c3ccc(NC(=O)C4CCN(S(=O)(=O)c5cccs5)C4)cc3)sc2c1 | 0.638888888888889
1032469 | O=C(Nc1nc2ccc(Cl)cc2s1)[C@@H]1CCCN1C(=O)c1cccs1 | 0.623188405797101
751668 | COc1ccc2nc(NC(=O)[C@@H]3CCCN3C(=O)c3cccs3)sc2c1 | 0.619718309859155
471318 | Cc1ccc2nc(-c3ccc(NC(=O)C4CN(S(=O)(=O)c5cccs5)C4)cc3)sc2c1 | 0.611111111111111
740754 | Cc1ccc(NC(=O)C2CCCN2C(=O)c2cccs2)cc1C | 0.606060606060606
732905 | O=C(Nc1ccc(S(=O)(=O)N2CCCC2)cc1)C1CCCN1C(=O)c1cccs1 | 0.602941176470588
1087495 | Cc1ccc(NC(=O)C2CCCN2C(=O)c2cccs2)c(C)c1 | 0.597014925373134
(10 rows)
Time: 5453.200 ms
chembl_14=# select * from get_mfp2_neighbors('Cc1ccc2nc(N(C)CC(=O)O)sc2c1') limit 10;
molregno | m | similarity
----------+-------------------------------------------------------+-------------------
412312 | Cc1ccc2nc(N(C)CCN(C)c3nc4ccc(C)cc4s3)sc2c1 | 0.692307692307692
470082 | CN(CC(=O)O)c1nc2cc([N+](=O)[O-])ccc2s1 | 0.583333333333333
1040255 | CC(=O)N(CCCN(C)C)c1nc2ccc(C)cc2s1 | 0.571428571428571
773946 | Cl.CC(=O)N(CCCN(C)C)c1nc2ccc(C)cc2s1 | 0.549019607843137
1044892 | Cc1ccc2nc(N(CCN(C)C)C(=O)c3cc(Cl)sc3Cl)sc2c1 | 0.518518518518518
1040496 | Cc1ccc2nc(N(CCCN(C)C)C(=O)CCc3ccccc3)sc2c1 | 0.517857142857143
1049393 | Cc1ccc2nc(N(CCCN(C)C)C(=O)CS(=O)(=O)c3ccccc3)sc2c1 | 0.517857142857143
441378 | Cc1ccc2nc(NC(=O)CCC(=O)O)sc2c1 | 0.510204081632653
1042958 | Cc1ccc2nc(N(CCN(C)C)C(=O)c3ccc4ccccc4c3)sc2c1 | 0.509090909090909
1047691 | Cc1ccc(S(=O)(=O)CC(=O)N(CCCN(C)C)c2nc3ccc(C)cc3s2)cc1 | 0.509090909090909
(10 rows)
Time: 1797.656 ms
Adjusting the similarity cutoff¶
By default, the minimum similarity returned with a similarity search is 0.5. This can be adjusted with the rdkit.tanimoto_threshold (and rdkit.dice_threshold) configuration variables:
chembl_14=# select count(*) from get_mfp2_neighbors('Cc1ccc2nc(N(C)CC(=O)O)sc2c1');
count
-------
18
(1 row)
Time: 1199.751 ms
chembl_14=# set rdkit.tanimoto_threshold=0.7;
SET
Time: 0.191 ms
chembl_14=# select count(*) from get_mfp2_neighbors('Cc1ccc2nc(N(C)CC(=O)O)sc2c1');
count
-------
0
(1 row)
Time: 826.058 ms
chembl_14=# set rdkit.tanimoto_threshold=0.6;
SET
Time: 0.220 ms
chembl_14=# select count(*) from get_mfp2_neighbors('Cc1ccc2nc(N(C)CC(=O)O)sc2c1');
count
-------
1
(1 row)
Time: 1092.303 ms
chembl_14=# set rdkit.tanimoto_threshold=0.5
chembl_14-# ;
SET
Time: 0.257 ms
chembl_14=# select count(*) from get_mfp2_neighbors('Cc1ccc2nc(N(C)CC(=O)O)sc2c1');
count
-------
18
(1 row)
Time: 1081.721 ms
Using the MCS code¶
The most straightforward use of the MCS code is to find the maximum common substructure of a group of molecules:
chembl_20=# select fmcs(m) from rdk.mols join compound_records using (molregno) where doc_id=3; fmcs
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
[#6]1(-[#7](-[#6](-[#6]2:[#6]:[#6]:[#6](:[#6]:[#6]:2)-[#7]-[#6](-[#6]2:[#6](-[#6]3:[#6]:[#6]:[#6]:[#6]:[#6]:3):[#6]:[#6]:[#6]:[#6]:2)=[#8])=[#8])-[#6]-[#6]-[#6]):[#6]:[#16]:[#6]:[#6]:1
(1 row)
chembl_20=# select fmcs(m) from rdk.mols join compound_records using (molregno) where doc_id=4;
fmcs
------------------------------------------------------------------------
[#6](-[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6])-[#7]-[#6]-[#6](-,:[#6])-,:[#6]
(1 row)
The same thing can be done with a SMILES column:
chembl_20=# select fmcs(canonical_smiles) from compound_structures join compound_records using (molregno) where doc_id=4;
fmcs
------------------------------------------------------------------------
[#6](-[#7]-[#6]-[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6])-[#6](-,:[#6])-,:[#6]
(1 row)
It’s also possible to adjust some of the parameters to the FMCS algorithm, though this is somewhat more painful as of this writing (the 2015_03_1 release). Here are a couple of examples:
chembl_20=# select fmcs_smiles(str,'{"Threshold":0.8}') from
chembl_20-# (select string_agg(m::text,' ') as str from rdk.mols
chembl_20(# join compound_records using (molregno) where doc_id=4) as str ;
fmcs_smiles
------------------------------------------------------------------------------------------------------------------------------------------------------------------
[#6]-[#6]-[#8]-[#6](-[#6](=[#8])-[#7]-[#6](-[#6])-[#6](-,:[#6])-,:[#6])-[#6](-[#8])-[#6](-[#8])-[#6](-[#8]-[#6]-[#6])-[#6]-[#7]-[#6](-[#6])-[#6](-,:[#6])-,:[#6]
(1 row)
chembl_20=# select fmcs_smiles(str,'{"AtomCompare":"Any"}') from
chembl_20-# (select string_agg(m::text,' ') as str from rdk.mols
chembl_20(# join compound_records using (molregno) where doc_id=4) as str ;
fmcs_smiles
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
[#6]-,:[#6,#7]-[#8,#6]-[#6,#7](-[#6,#8]-[#7,#6]-,:[#6,#7]-,:[#6,#7]-,:[#7,#6]-,:[#6])-[#6,#7]-[#6]-[#6](-[#8,#6]-[#6])-[#6,#7]-[#7,#6]-[#6]-,:[#6,#8]-,:[#7,#6]-,:[#6]
(1 row)
Note The combination of "AtomCompare":"Any"
and a value of "Threshold"
that is less than 1.0 does a quite generic search and can results in very long search times.
Using "Timeout"
with this combination is recommended:
chembl_20=# select fmcs_smiles(str,'{"AtomCompare":"Any","CompleteRingsOnly":true,"Threshold":0.8,"Timeout":60}') from
chembl_20-# (select string_agg(m::text,' ') as str from rdk.mols
chembl_20(# join compound_records using (molregno) where doc_id=3) as str ;
WARNING: findMCS timed out, result is not maximal
fmcs_smiles
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
[#8]=[#6](-[#7]-[#6]1:[#6]:[#6]:[#6](:[#6]:[#6]:1)-[#6](=[#8])-[#7]1-[#6]-[#6]-[#6]-[#6,#7]-[#6]2:[#6]-1:[#6]:[#6]:[#16]:2)-[#6]1:[#6]:[#6]:[#6]:[#6]:[#6]:1-[#6]1:[#6]:[#6]:[#6]:[#6]:[#6]:1
(1 row)
Available parameters and their default values are:
- MaximizeBonds (true)
- Threshold (1.0)
- Timeout (-1, no timeout)
- MatchValences (false)
- MatchChiralTag (false) Applies to atoms
- RingMatchesRingOnly (false)
- CompleteRingsOnly (false)
- MatchStereo (false) Applies to bonds
- AtomCompare (“Elements”) can be “Elements”, “Isotopes”, or “Any”
- BondCompare (“Order”) can be “Order”, “OrderExact”, or “Any”
Reference Guide¶
New Types¶
- mol : an rdkit molecule. Can be created from a SMILES via direct type conversion, for example: ‘c1ccccc1’::mol creates a molecule from the SMILES ‘c1ccccc1’
- qmol : an rdkit molecule containing query features (i.e. constructed from SMARTS). Can be created from a SMARTS via direct type conversion, for example: ‘c1cccc[c,n]1’::qmol creates a query molecule from the SMARTS ‘c1cccc[c,n]1’
- sfp : a sparse count vector fingerprint (SparseIntVect in C++ and Python)
- bfp : a bit vector fingerprint (ExplicitBitVect in C++ and Python)
Parameters¶
- rdkit.tanimoto_threshold : threshold value for the Tanimoto similarity operator. Searches done using Tanimoto similarity will only return results with a similarity of at least this value.
- rdkit.dice_threshold : threshold value for the Dice similiarty operator. Searches done using Dice similarity will only return results with a similarity of at least this value.
- rdkit.do_chiral_sss : toggles whether or not stereochemistry is used in substructure matching. (available from 2013_03 release).
- rdkit.sss_fp_size : the size (in bits) of the fingerprint used for substructure screening.
- rdkit.morgan_fp_size : the size (in bits) of morgan fingerprints
- rdkit.featmorgan_fp_size : the size (in bits) of featmorgan fingerprints
- rdkit.layered_fp_size : the size (in bits) of layered fingerprints
- rdkit.rdkit_fp_size : the size (in bits) of RDKit fingerprints
- rdkit.torsion_fp_size : the size (in bits) of topological torsion bit vector fingerprints
- rdkit.atompair_fp_size : the size (in bits) of atom pair bit vector fingerprints
- rdkit.avalon_fp_size : the size (in bits) of avalon fingerprints
Operators¶
Similarity search¶
- % : operator used for similarity searches using Tanimoto similarity. Returns whether or not the Tanimoto similarity between two fingerprints (either two sfp or two bfp values) exceeds rdkit.tanimoto_threshold.
- # : operator used for similarity searches using Dice similarity. Returns whether or not the Dice similarity between two fingerprints (either two sfp or two bfp values) exceeds rdkit.dice_threshold.
- <%> : used for Tanimoto KNN searches (to return ordered lists of neighbors).
- <#> : used for Dice KNN searches (to return ordered lists of neighbors).
Substructure and exact structure search¶
- @> : substructure search operator. Returns whether or not the mol or qmol on the right is a substructure of the mol on the left.
- <@ : substructure search operator. Returns whether or not the mol or qmol on the left is a substructure of the mol on the right.
- @= : returns whether or not two molecules are the same.
Molecule comparison¶
- < : returns whether or not the left mol is less than the right mol
- > : returns whether or not the left mol is greater than the right mol
- = : returns whether or not the left mol is equal to the right mol
- <= : returns whether or not the left mol is less than or equal to the right mol
- >= : returns whether or not the left mol is greater than or equal to the right mol
Note Two molecules are compared by making the following comparisons in order. Later comparisons are only made if the preceding values are equal:
# Number of atoms # Number of bonds # Molecular weight # Number of rings
If all of the above are the same and the second molecule is a substructure of the first, the molecules are declared equal, Otherwise (should not happen) the first molecule is arbitrarily defined to be less than the second.
There are additional operators defined in the cartridge, but these are used for internal purposes.
Using the Cartridge from Python¶
The recommended adapter for connecting to postgresql is pyscopg2 (https://pypi.python.org/pypi/psycopg2).
Here’s an example of connecting to our local copy of ChEMBL and doing a basic substructure search:
>>> import psycopg2
>>> conn = psycopg2.connect(database='chembl_16')
>>> curs = conn.cursor()
>>> curs.execute('select * from rdk.mols where m@>%s',('c1cccc2c1nncc2',))
>>> curs.fetchone()
(9830, 'CC(C)Sc1ccc(CC2CCN(C3CCN(C(=O)c4cnnc5ccccc54)CC3)CC2)cc1')
That returns a SMILES for each molecule. If you plan to do more work with the molecules after retrieving them, it is much more efficient to ask postgresql to give you the molecules in pickled form:
>>> curs.execute('select molregno,mol_send(m) from rdk.mols where m@>%s',('c1cccc2c1nncc2',))
>>> row = curs.fetchone()
>>> row
(9830, <read-only buffer for 0x...>)
These pickles can then be converted into molecules:
>>> from rdkit import Chem
>>> m = Chem.Mol(str(row[1]))
>>> Chem.MolToSmiles(m,True)
'CC(C)Sc1ccc(CC2CCN(C3CCN(C(=O)c4cnnc5ccccc54)CC3)CC2)cc1'
License¶
This document is copyright (C) 2013 by Greg Landrum
This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.
The intent of this license is similar to that of the RDKit itself. In simple words: “Do whatever you want with it, but please give us some credit.”