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<li class="toctree-l1"><a class="reference internal" href="Overview.html">An overview of the RDKit</a></li>
<li class="toctree-l1"><a class="reference internal" href="Install.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="GettingStartedInPython.html">Getting Started with the RDKit in Python</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Cookbook.html">RDKit Cookbook</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="">The RDKit database cartridge</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#what-is-this">What is this?</a></li>
<li class="toctree-l2"><a class="reference internal" href="#tutorial">Tutorial</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#introduction">Introduction</a></li>
<li class="toctree-l3"><a class="reference internal" href="#creating-databases">Creating databases</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#configuration">Configuration</a></li>
<li class="toctree-l4"><a class="reference internal" href="#creating-a-database-from-a-file">Creating a database from a file</a></li>
<li class="toctree-l4"><a class="reference internal" href="#loading-chembl">Loading ChEMBL</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#substructure-searches">Substructure searches</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#smarts-based-queries">SMARTS-based queries</a></li>
<li class="toctree-l4"><a class="reference internal" href="#using-stereochemistry">Using Stereochemistry</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="#similarity-searches">Similarity searches</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#adjusting-the-similarity-cutoff">Adjusting the similarity cutoff</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#using-the-mcs-code">Using the MCS code</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#new-types">New Types</a></li>
<li class="toctree-l3"><a class="reference internal" href="#parameters">Parameters</a></li>
<li class="toctree-l3"><a class="reference internal" href="#operators">Operators</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#similarity-search">Similarity search</a></li>
<li class="toctree-l4"><a class="reference internal" href="#substructure-and-exact-structure-search">Substructure and exact structure search</a></li>
<li class="toctree-l4"><a class="reference internal" href="#molecule-comparison">Molecule comparison</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#functions">Functions</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#fingerprint-related">Fingerprint Related</a></li>
<li class="toctree-l4"><a class="reference internal" href="#molecule-related">Molecule Related</a></li>
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<div class="section" id="the-rdkit-database-cartridge">
<h1>The RDKit database cartridge<a class="headerlink" href="#the-rdkit-database-cartridge" title="Permalink to this headline">¶</a></h1>
<div class="section" id="what-is-this">
<h2>What is this?<a class="headerlink" href="#what-is-this" title="Permalink to this headline">¶</a></h2>
<p>This document is a tutorial and reference guide for the RDKit PostgreSQL cartridge.</p>
<p>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: <a class="reference external" href="mailto:rdkit-discuss%40lists.sourceforge.net">rdkit-discuss<span>@</span>lists<span>.</span>sourceforge<span>.</span>net</a>
(you will need to subscribe first)</p>
</div>
<div class="section" id="tutorial">
<h2>Tutorial<a class="headerlink" href="#tutorial" title="Permalink to this headline">¶</a></h2>
<div class="section" id="introduction">
<h3>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h3>
</div>
<div class="section" id="creating-databases">
<h3>Creating databases<a class="headerlink" href="#creating-databases" title="Permalink to this headline">¶</a></h3>
<div class="section" id="configuration">
<h4>Configuration<a class="headerlink" href="#configuration" title="Permalink to this headline">¶</a></h4>
<p>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.</p>
<p>To improve performance while loading the database and building the index,
I changed a couple of postgres configuration settings in <cite>postgresql.conf</cite></p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">fsync</span> <span class="o">=</span> <span class="n">off</span> <span class="c"># turns forced synchronization on or off</span>
<span class="n">synchronous_commit</span> <span class="o">=</span> <span class="n">off</span> <span class="c"># immediate fsync at commit</span>
<span class="n">full_page_writes</span> <span class="o">=</span> <span class="n">off</span> <span class="c"># recover from partial page writes</span>
</pre></div>
</div>
<p>And to improve search performance, I allowed postgresql to use more memory than the
extremely conservative default settings:</p>
<div class="highlight-python"><div class="highlight"><pre>shared_buffers = 2048MB # min 128kB
# (change requires restart)
work_mem = 128MB # min 64kB
</pre></div>
</div>
</div>
<div class="section" id="creating-a-database-from-a-file">
<h4>Creating a database from a file<a class="headerlink" href="#creating-a-database-from-a-file" title="Permalink to this headline">¶</a></h4>
<p>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
<a class="reference external" href="http://www.emolecules.com/doc/plus/download-database.php">http://www.emolecules.com/doc/plus/download-database.php</a></p>
<p>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.</p>
<p>First create the database and install the cartridge:</p>
<div class="highlight-python"><div class="highlight"><pre>~/RDKit_trunk/Data/emolecules > createdb emolecules
~/RDKit_trunk/Data/emolecules > psql -c 'create extension rdkit' emolecules
</pre></div>
</div>
<p>Now create and populate a table holding the raw data:</p>
<div class="highlight-python"><div class="highlight"><pre>~/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
</pre></div>
</div>
<p>Create the molecule table, but only for SMILES that the RDKit accepts:</p>
<div class="highlight-python"><div class="highlight"><pre>~/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
</pre></div>
</div>
<p>The last step is only required if you plan to do substructure searches.</p>
</div>
<div class="section" id="loading-chembl">
<h4>Loading ChEMBL<a class="headerlink" href="#loading-chembl" title="Permalink to this headline">¶</a></h4>
<p>Start by downloading and installing the postgresql dump from the ChEMBL website
<a class="reference external" href="ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest">ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest</a></p>
<p>Connect to the database, install the cartridge, and create the schema that we’ll use:</p>
<div class="highlight-python"><div class="highlight"><pre>chembl_14=# create extension if not exists rdkit;
chembl_14=# create schema rdk;
</pre></div>
</div>
<p>Create the molecules and build the substructure search index:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
<p>Create some fingerprints and build the similarity search index:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
</div>
</div>
<div class="section" id="substructure-searches">
<h3>Substructure searches<a class="headerlink" href="#substructure-searches" title="Permalink to this headline">¶</a></h3>
<p>Example query molecules taken from the <a class="reference external" href="http://www.emolecules.com/">eMolecules home page</a>:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
<p>Notice that the last two queries are starting to take a while to execute and count all the results.</p>
<p>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.</p>
<p>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:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
<div class="section" id="smarts-based-queries">
<h4>SMARTS-based queries<a class="headerlink" href="#smarts-based-queries" title="Permalink to this headline">¶</a></h4>
<p>Oxadiazole or thiadiazole:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
<p>This is slower than the pure SMILES query, this is generally true of SMARTS-based queries.</p>
</div>
<div class="section" id="using-stereochemistry">
<h4>Using Stereochemistry<a class="headerlink" href="#using-stereochemistry" title="Permalink to this headline">¶</a></h4>
<p>Note that by default stereochemistry is not taken into account when doing substructure queries:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
<p>This can be changed using the <cite>rdkit.do_chiral_sss</cite> configuration variable:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
</div>
</div>
<div class="section" id="similarity-searches">
<h3>Similarity searches<a class="headerlink" href="#similarity-searches" title="Permalink to this headline">¶</a></h3>
<p>Basic similarity searching:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
<p>Usually we’d like to find a sorted listed of neighbors along with the accompanying SMILES.
This SQL function makes that pattern easy:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
<div class="section" id="adjusting-the-similarity-cutoff">
<h4>Adjusting the similarity cutoff<a class="headerlink" href="#adjusting-the-similarity-cutoff" title="Permalink to this headline">¶</a></h4>
<p>By default, the minimum similarity returned with a similarity search is 0.5. This can be adjusted with the <cite>rdkit.tanimoto_threshold</cite>
(and <cite>rdkit.dice_threshold</cite>) configuration variables:</p>
<div class="highlight-python"><div class="highlight"><pre>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
</pre></div>
</div>
</div>
</div>
<div class="section" id="using-the-mcs-code">
<h3>Using the MCS code<a class="headerlink" href="#using-the-mcs-code" title="Permalink to this headline">¶</a></h3>
<p>The most straightforward use of the MCS code is to find the maximum common substructure of a group of molecules:</p>
<div class="highlight-python"><div class="highlight"><pre>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)
</pre></div>
</div>
<p>The same thing can be done with a SMILES column:</p>
<div class="highlight-python"><div class="highlight"><pre>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)
</pre></div>
</div>
<p>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:</p>
<div class="highlight-python"><div class="highlight"><pre>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)
</pre></div>
</div>
<p><em>Note</em> The combination of <tt class="docutils literal"><span class="pre">"AtomCompare":"Any"</span></tt> and a value of <tt class="docutils literal"><span class="pre">"Threshold"</span></tt> that is less than 1.0 does a quite generic search and can results in very long search times.
Using <tt class="docutils literal"><span class="pre">"Timeout"</span></tt> with this combination is recommended:</p>
<div class="highlight-python"><div class="highlight"><pre>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)
</pre></div>
</div>
<p>Available parameters and their default values are:</p>
<blockquote>
<div><ul class="simple">
<li>MaximizeBonds (true)</li>
<li>Threshold (1.0)</li>
<li>Timeout (-1, no timeout)</li>
<li>MatchValences (false)</li>
<li>MatchChiralTag (false) Applies to atoms</li>
<li>RingMatchesRingOnly (false)</li>
<li>CompleteRingsOnly (false)</li>
<li>MatchStereo (false) Applies to bonds</li>
<li>AtomCompare (“Elements”) can be “Elements”, “Isotopes”, or “Any”</li>
<li>BondCompare (“Order”) can be “Order”, “OrderExact”, or “Any”</li>
</ul>
</div></blockquote>
</div>
</div>
<div class="section" id="reference-guide">
<h2>Reference Guide<a class="headerlink" href="#reference-guide" title="Permalink to this headline">¶</a></h2>
<div class="section" id="new-types">
<h3>New Types<a class="headerlink" href="#new-types" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><cite>mol</cite> : an rdkit molecule. Can be created from a SMILES via direct type conversion, for example: <cite>‘c1ccccc1’::mol</cite> creates a molecule from the SMILES <cite>‘c1ccccc1’</cite></li>
<li><cite>qmol</cite> : an rdkit molecule containing query features (i.e. constructed from SMARTS). Can be created from a SMARTS via direct type conversion, for example: <cite>‘c1cccc[c,n]1’::qmol</cite> creates a query molecule from the SMARTS <cite>‘c1cccc[c,n]1’</cite></li>
<li><cite>sfp</cite> : a sparse count vector fingerprint (<cite>SparseIntVect</cite> in C++ and Python)</li>
<li><cite>bfp</cite> : a bit vector fingerprint (<cite>ExplicitBitVect</cite> in C++ and Python)</li>
</ul>
</div>
<div class="section" id="parameters">
<h3>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><cite>rdkit.tanimoto_threshold</cite> : threshold value for the Tanimoto similarity operator. Searches done using Tanimoto similarity will only return results with a similarity of at least this value.</li>
<li><cite>rdkit.dice_threshold</cite> : threshold value for the Dice similiarty operator. Searches done using Dice similarity will only return results with a similarity of at least this value.</li>
<li><cite>rdkit.do_chiral_sss</cite> : toggles whether or not stereochemistry is used in substructure matching. (<em>available from 2013_03 release</em>).</li>
<li><cite>rdkit.sss_fp_size</cite> : the size (in bits) of the fingerprint used for substructure screening.</li>
<li><cite>rdkit.morgan_fp_size</cite> : the size (in bits) of morgan fingerprints</li>
<li><cite>rdkit.featmorgan_fp_size</cite> : the size (in bits) of featmorgan fingerprints</li>
<li><cite>rdkit.layered_fp_size</cite> : the size (in bits) of layered fingerprints</li>
<li><cite>rdkit.rdkit_fp_size</cite> : the size (in bits) of RDKit fingerprints</li>
<li><cite>rdkit.torsion_fp_size</cite> : the size (in bits) of topological torsion bit vector fingerprints</li>
<li><cite>rdkit.atompair_fp_size</cite> : the size (in bits) of atom pair bit vector fingerprints</li>
<li><cite>rdkit.avalon_fp_size</cite> : the size (in bits) of avalon fingerprints</li>
</ul>
</div>
<div class="section" id="operators">
<h3>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">¶</a></h3>
<div class="section" id="similarity-search">
<h4>Similarity search<a class="headerlink" href="#similarity-search" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><cite>%</cite> : operator used for similarity searches using Tanimoto similarity. Returns whether or not the Tanimoto similarity between two fingerprints (either two <cite>sfp</cite> or two <cite>bfp</cite> values) exceeds <cite>rdkit.tanimoto_threshold</cite>.</li>
<li><cite>#</cite> : operator used for similarity searches using Dice similarity. Returns whether or not the Dice similarity between two fingerprints (either two <cite>sfp</cite> or two <cite>bfp</cite> values) exceeds <cite>rdkit.dice_threshold</cite>.</li>
<li><cite><%></cite> : used for Tanimoto KNN searches (to return ordered lists of neighbors).</li>
<li><cite><#></cite> : used for Dice KNN searches (to return ordered lists of neighbors).</li>
</ul>
</div>
<div class="section" id="substructure-and-exact-structure-search">
<h4>Substructure and exact structure search<a class="headerlink" href="#substructure-and-exact-structure-search" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><cite>@></cite> : substructure search operator. Returns whether or not the <cite>mol</cite> or <cite>qmol</cite> on the right is a substructure of the <cite>mol</cite> on the left.</li>
<li><cite><@</cite> : substructure search operator. Returns whether or not the <cite>mol</cite> or <cite>qmol</cite> on the left is a substructure of the <cite>mol</cite> on the right.</li>
<li><cite>@=</cite> : returns whether or not two molecules are the same.</li>
</ul>
</div>
<div class="section" id="molecule-comparison">
<h4>Molecule comparison<a class="headerlink" href="#molecule-comparison" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><cite><</cite> : returns whether or not the left <cite>mol</cite> is less than the right <cite>mol</cite></li>
<li><cite>></cite> : returns whether or not the left <cite>mol</cite> is greater than the right <cite>mol</cite></li>
<li><cite>=</cite> : returns whether or not the left <cite>mol</cite> is equal to the right <cite>mol</cite></li>
<li><cite><=</cite> : returns whether or not the left <cite>mol</cite> is less than or equal to the right <cite>mol</cite></li>
<li><cite>>=</cite> : returns whether or not the left <cite>mol</cite> is greater than or equal to the right <cite>mol</cite></li>
</ul>
<p><em>Note</em> Two molecules are compared by making the following comparisons in order. Later comparisons are only made if the preceding values are equal:</p>
<p># Number of atoms
# Number of bonds
# Molecular weight
# Number of rings</p>
<p>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.</p>
<p>There are additional operators defined in the cartridge, but these are used for internal purposes.</p>
</div>
</div>
<div class="section" id="functions">
<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h3>
<div class="section" id="fingerprint-related">
<h4>Fingerprint Related<a class="headerlink" href="#fingerprint-related" title="Permalink to this headline">¶</a></h4>
<div class="section" id="generating-fingerprints">
<h5>Generating fingerprints<a class="headerlink" href="#generating-fingerprints" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>morgan_fp(mol,int default 2)</cite> : returns an <cite>sfp</cite> which is the count-based Morgan fingerprint for a molecule using connectivity invariants. The second argument provides the radius. This is an ECFP-like fingerprint.</li>
<li><cite>morganbv_fp(mol,int default 2)</cite> : returns a <cite>bfp</cite> which is the bit vector Morgan fingerprint for a molecule using connectivity invariants. The second argument provides the radius. This is an ECFP-like fingerprint.</li>
<li><cite>featmorgan_fp(mol,int default 2)</cite> : returns an <cite>sfp</cite> which is the count-based Morgan fingerprint for a molecule using chemical-feature invariants. The second argument provides the radius. This is an FCFP-like fingerprint.</li>
<li><cite>featmorganbv_fp(mol,int default 2)</cite> : returns a <cite>bfp</cite> which is the bit vector Morgan fingerprint for a molecule using chemical-feature invariants. The second argument provides the radius. This is an FCFP-like fingerprint.</li>
<li><cite>rdkit_fp(mol)</cite> : returns a <cite>bfp</cite> which is the RDKit fingerprint for a molecule. This is a daylight-fingerprint using hashed molecular subgraphs.</li>
<li><cite>atompair_fp(mol)</cite> : returns an <cite>sfp</cite> which is the count-based atom-pair fingerprint for a molecule.</li>
<li><cite>atompairbv_fp(mol)</cite> : returns a <cite>bfp</cite> which is the bit vector atom-pair fingerprint for a molecule.</li>
<li><cite>torsion_fp(mol)</cite> : returns an <cite>sfp</cite> which is the count-based topological-torsion fingerprint for a molecule.</li>
<li><cite>torsionbv_fp(mol)</cite> : returns a <cite>bfp</cite> which is the bit vector topological-torsion fingerprint for a molecule.</li>
<li><cite>layered_fp(mol)</cite> : returns a <cite>bfp</cite> which is the layered fingerprint for a molecule. This is an experimental substructure fingerprint using hashed molecular subgraphs.</li>
<li><cite>maccs_fp(mol)</cite> : returns a <cite>bfp</cite> which is the MACCS fingerprint for a molecule (<em>available from 2013_01 release</em>).</li>
</ul>
</div>
<div class="section" id="working-with-fingerprints">
<h5>Working with fingerprints<a class="headerlink" href="#working-with-fingerprints" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>tanimoto_sml(fp,fp)</cite> : returns the Tanimoto similarity between two fingerprints of the same type (either two <cite>sfp</cite> or two <cite>bfp</cite> values).</li>
<li><cite>dice_sml(fp,fp)</cite> : returns the Dice similarity between two fingerprints of the same type (either two <cite>sfp</cite> or two <cite>bfp</cite> values).</li>
<li><cite>size(bfp)</cite> : returns the length of (number of bits in) a <cite>bfp</cite>.</li>
<li><cite>add(sfp,sfp)</cite> : returns an <cite>sfp</cite> formed by the element-wise addition of the two <cite>sfp</cite> arguments.</li>
<li><cite>subtract(sfp,sfp)</cite> : returns an <cite>sfp</cite> formed by the element-wise subtraction of the two <cite>sfp</cite> arguments.</li>
<li><cite>all_values_lt(sfp,int)</cite> : returns a boolean indicating whether or not all elements of the <cite>sfp</cite> argument are less than the <cite>int</cite> argument.</li>
<li><cite>all_values_gt(sfp,int)</cite> : returns a boolean indicating whether or not all elements of the <cite>sfp</cite> argument are greater than the <cite>int</cite> argument.</li>
</ul>
</div>
<div class="section" id="fingerprint-i-o">
<h5>Fingerprint I/O<a class="headerlink" href="#fingerprint-i-o" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>bfp_to_binary_text(bfp)</cite> : returns a bytea with the binary string representation of the fingerprint that can be converted back into an RDKit fingerprint in other software. (<em>available from Q3 2012 (2012_09) release</em>)</li>
<li><cite>bfp_from_binary_text(bytea)</cite> : constructs a bfp from a binary string representation of the fingerprint. (<em>available from Q3 2012 (2012_09) release</em>)</li>
</ul>
</div>
</div>
<div class="section" id="molecule-related">
<h4>Molecule Related<a class="headerlink" href="#molecule-related" title="Permalink to this headline">¶</a></h4>
<div class="section" id="molecule-i-o-and-validation">
<h5>Molecule I/O and Validation<a class="headerlink" href="#molecule-i-o-and-validation" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>is_valid_smiles(smiles)</cite> : returns whether or not a SMILES string produces a valid RDKit molecule.</li>
<li><cite>is_valid_ctab(ctab)</cite> : returns whether or not a CTAB (mol block) string produces a valid RDKit molecule.</li>
<li><cite>is_valid_smarts(smarts)</cite> : returns whether or not a SMARTS string produces a valid RDKit molecule.</li>
<li><cite>is_valid_mol_pkl(bytea)</cite> : returns whether or not a binary string (bytea) can be converted into an RDKit molecule. (<em>available from Q3 2012 (2012_09) release</em>)</li>
<li><cite>mol_from_smiles(smiles)</cite> : returns a molecule for a SMILES string, NULL if the molecule construction fails.</li>
<li><cite>mol_from_smarts(smarts)</cite> : returns a molecule for a SMARTS string, NULL if the molecule construction fails.</li>
<li><cite>mol_from_ctab(ctab, bool default false)</cite> : returns a molecule for a CTAB (mol block) string, NULL if the molecule construction fails. The optional second argument controls whether or not the molecule’s coordinates are saved.</li>
<li><cite>mol_from_pkl(bytea)</cite> : returns a molecule for a binary string (bytea), NULL if the molecule construction fails. (<em>available from Q3 2012 (2012_09) release</em>)</li>
<li><cite>qmol_from_smiles(smiles)</cite> : returns a query molecule for a SMILES string, NULL if the molecule construction fails. Explicit Hs in the SMILES are converted into query features on the attached atom.</li>
<li><cite>qmol_from_ctab(ctab, bool default false)</cite> : returns a query molecule for a CTAB (mol block) string, NULL if the molecule construction fails. Explicit Hs in the SMILES are converted into query features on the attached atom. The optional second argument controls whether or not the molecule’s coordinates are saved.</li>
<li><cite>mol_to_smiles(mol)</cite> : returns the canonical SMILES for a molecule.</li>
<li><cite>mol_to_smarts(mol)</cite> : returns SMARTS string for a molecule.</li>
<li><cite>mol_to_pkl(mol)</cite> : returns binary string (bytea) for a molecule. (<em>available from Q3 2012 (2012_09) release</em>)</li>
<li><cite>mol_to_ctab(mol,bool default true)</cite> : returns a CTAB (mol block) string for a molecule. The optional second argument controls whether or not 2D coordinates will be generated for molecules that don’t have coordinates. (<em>available from the 2014_03 release</em>)</li>
</ul>
</div>
<div class="section" id="substructure-operations">
<h5>Substructure operations<a class="headerlink" href="#substructure-operations" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>substruct(mol,mol)</cite> : returns whether or not the second mol is a substructure of the first.</li>
<li><cite>substruct_count(mol,mol,bool default true)</cite> : returns the number of substructure matches between the second molecule and the first. The third argument toggles whether or not the matches are uniquified. (<em>available from 2013_03 release</em>)</li>
</ul>
</div>
<div class="section" id="descriptors">
<h5>Descriptors<a class="headerlink" href="#descriptors" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>mol_amw(mol)</cite> : returns the AMW for a molecule.</li>
<li><cite>mol_logp(mol)</cite> : returns the MolLogP for a molecule.</li>
<li><cite>mol_tpsa(mol)</cite> : returns the topological polar surface area for a molecule (<em>available from Q1 2011 (2011_03) release</em>).</li>
<li><cite>mol_fractioncsp3(mol)</cite> : returns the fraction of carbons that are sp3 hybridized (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_hba(mol)</cite> : returns the number of Lipinski H-bond acceptors (i.e. number of Os and Ns) for a molecule.</li>
<li><cite>mol_hbd(mol)</cite> : returns the number of Lipinski H-bond donors (i.e. number of Os and Ns that have at least one H) for a molecule.</li>
<li><cite>mol_numatoms(mol)</cite> : returns the total number of atoms in a molecule.</li>
<li><cite>mol_numheavyatoms(mol)</cite> : returns the number of heavy atoms in a molecule.</li>
<li><cite>mol_numrotatablebonds(mol)</cite> : returns the number of rotatable bonds in a molecule (<em>available from Q1 2011 (2011_03) release</em>).</li>
<li><cite>mol_numheteroatoms(mol)</cite> : returns the number of heteroatoms in a molecule (<em>available from Q1 2011 (2011_03) release</em>).</li>
<li><cite>mol_numrings(mol)</cite> : returns the number of rings in a molecule (<em>available from Q1 2011 (2011_03) release</em>).</li>
<li><cite>mol_numaromaticrings(mol)</cite> : returns the number of aromatic rings in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numaliphaticrings(mol)</cite> : returns the number of aliphatic (at least one non-aromatic bond) rings in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numsaturatedrings(mol)</cite> : returns the number of saturated rings in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numaromaticheterocycles(mol)</cite> : returns the number of aromatic heterocycles in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numaliphaticheterocycles(mol)</cite> : returns the number of aliphatic (at least one non-aromatic bond) heterocycles in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numsaturatedheterocycles(mol)</cite> : returns the number of saturated heterocycles in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numaromaticcarbocycles(mol)</cite> : returns the number of aromatic carbocycles in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numaliphaticcarbocycles(mol)</cite> : returns the number of aliphatic (at least one non-aromatic bond) carbocycles in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_numsaturatedcarbocycles(mol)</cite> : returns the number of saturated carbocycles in a molecule (<em>available from 2013_03 release</em>).</li>
<li><cite>mol_inchi(mol)</cite> : returns an InChI for the molecule. (<em>available from the 2011_06 release, requires that the RDKit be built with InChI support</em>).</li>
<li><cite>mol_inchikey(mol)</cite> : returns an InChI key for the molecule. (<em>available from the 2011_06 release, requires that the RDKit be built with InChI support</em>).</li>
<li><cite>mol_formula(mol,bool default false, bool default true)</cite> : returns a string with the molecular formula. The second argument controls whether isotope information is included in the formula; the third argument controls whether “D” and “T” are used instead of [2H] and [3H].</li>
</ul>
<p>(<em>available from the 2014_03 release</em>)</p>
</div>
<div class="section" id="connectivity-descriptors">
<h5>Connectivity Descriptors<a class="headerlink" href="#connectivity-descriptors" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>mol_chi0v(mol)</cite> - <cite>mol_chi4v(mol)</cite> : returns the ChiXv value for a molecule for X=0-4 (<em>available from 2012_01 release</em>).</li>
<li><cite>mol_chi0n(mol)</cite> - <cite>mol_chi4n(mol)</cite> : returns the ChiXn value for a molecule for X=0-4 (<em>available from 2012_01 release</em>).</li>
<li><cite>mol_kappa1(mol)</cite> - <cite>mol_kappa3(mol)</cite> : returns the kappaX value for a molecule for X=1-3 (<em>available from 2012_01 release</em>).</li>
</ul>
</div>
<div class="section" id="mcs">
<h5>MCS<a class="headerlink" href="#mcs" title="Permalink to this headline">¶</a></h5>
<ul class="simple">
<li><cite>fmcs(mols)</cite> : an aggregation function that calculates the MCS for a set of molecules</li>
<li><cite>fmcs_smiles(text, json default ‘’)</cite> : calculates the MCS for a space-separated set of SMILES. The optional json argument is used to provide parameters to the MCS code.</li>
</ul>
</div>
</div>
<div class="section" id="other">
<h4>Other<a class="headerlink" href="#other" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><cite>rdkit_version()</cite> : returns a string with the cartridge version number.</li>
</ul>
<p>There are additional functions defined in the cartridge, but these are used for internal purposes.</p>
</div>
</div>
</div>
<div class="section" id="using-the-cartridge-from-python">
<h2>Using the Cartridge from Python<a class="headerlink" href="#using-the-cartridge-from-python" title="Permalink to this headline">¶</a></h2>
<p>The recommended adapter for connecting to postgresql is pyscopg2
(<a class="reference external" href="https://pypi.python.org/pypi/psycopg2">https://pypi.python.org/pypi/psycopg2</a>).</p>
<p>Here’s an example of connecting to our local copy of ChEMBL and doing
a basic substructure search:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">psycopg2</span>
<span class="gp">>>> </span><span class="n">conn</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">database</span><span class="o">=</span><span class="s">'chembl_16'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">curs</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s">'select * from rdk.mols where m@></span><span class="si">%s</span><span class="s">'</span><span class="p">,(</span><span class="s">'c1cccc2c1nncc2'</span><span class="p">,))</span>
<span class="gp">>>> </span><span class="n">curs</span><span class="o">.</span><span class="n">fetchone</span><span class="p">()</span>
<span class="go">(9830, 'CC(C)Sc1ccc(CC2CCN(C3CCN(C(=O)c4cnnc5ccccc54)CC3)CC2)cc1')</span>
</pre></div>
</div>
<p>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:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s">'select molregno,mol_send(m) from rdk.mols where m@></span><span class="si">%s</span><span class="s">'</span><span class="p">,(</span><span class="s">'c1cccc2c1nncc2'</span><span class="p">,))</span>
<span class="gp">>>> </span><span class="n">row</span> <span class="o">=</span> <span class="n">curs</span><span class="o">.</span><span class="n">fetchone</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">row</span>
<span class="go">(9830, <read-only buffer for 0x...>)</span>
</pre></div>
</div>
<p>These pickles can then be converted into molecules:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
<span class="gp">>>> </span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="bp">True</span><span class="p">)</span>
<span class="go">'CC(C)Sc1ccc(CC2CCN(C3CCN(C(=O)c4cnnc5ccccc54)CC3)CC2)cc1'</span>
</pre></div>
</div>
</div>
<div class="section" id="license">
<h2>License<a class="headerlink" href="#license" title="Permalink to this headline">¶</a></h2>
<p>This document is copyright (C) 2013 by Greg Landrum</p>
<p>This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 License.
To view a copy of this license, visit <a class="reference external" href="http://creativecommons.org/licenses/by-sa/3.0/">http://creativecommons.org/licenses/by-sa/3.0/</a> or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.</p>
<p>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.”</p>
</div>
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