Some thoughts on the performance of the RDKit cartridge
EDITED on 23.01.2020 John Mayfield pointed out that the way I had constructed the set of 10 million molecules wasn’t reproducible. This update fixes that, but it also changes the results.
It’s been a while since I did a post about the cartridge! This one is a little bit ranty, but hopefully it’s still useful.
Some background and disclaimers
I normally do performance testing on the cartridge using ChEMBL, but since that’s “only” around 1.7 million compounds I wanted to try something a bit bigger. But how big? I don’t see the point in shoving 100 million compounds (or a billion) into PostgreSQL (there’s a mini-rant explaining this at the bottom of the post), so I went with 10 million. That’s more than five times bigger than ChEMBL, but it’s still not unimaginable that you’d actually have a relational schema with a molecule table this size that has data about most of those molecules in separate tables (again, see the mini-rant).
My goal here is to get a sense for what kind of performance you can expect for doing “normal” chemical queries using a local PostgreSQL instance without having to spend time optimizing the database or hardware configuration. I generated all the numbers below on my Linux desktop - which is 4-5 years old at this point - using an out-of-the-box install of PostgreSQL 12 (the only configuration tuning was to increase the size of shared_buffers to 4096M and work_mem as described in the RDKit docs). I was also using the machine for other things (like reading email or writing this post in Chrome) while these queries ran, so the numbers are really just ballpark estimates.
So which molecules to use? Rather than agonizing overly much about this, I just grabbed a PubChem Compound SMILES dump and took the first 10 million molecules from that.
I hedged above by saying “normal” chemical queries. I have a longer rant about that topic that I will spare you the details of, but I generally think that including worst-case queries (i.e. things that return tens or hundreds of thousands of rows) in real-world benchmarking examples is pointless. It’s definitely useful to understand the worst-case performance of a system, but that’s not what I’m doing here. So my queries will often limit the number of rows returned to what I think is a “reasonable” number for interactive usage.
Creating the database
This tends to be time consuming, particularly creating the molecule index. Start by creating the database from the command line and loading all of the raw data:
glandrum@otter:/tmp/pubchem_load$ createdb pubchem_compound
glandrum@otter:/tmp/pubchem_load$ psql -c 'create extension rdkit' pubchem_compound
CREATE EXTENSION
glandrum@otter:/tmp/pubchem_load$ psql -c 'create table raw_data (id SERIAL,cid integer, smiles text)' pubchem_compound
CREATE TABLE
glandrum@otter:/tmp/pubchem_load$ zcat CID-SMILES.gz | sed 's/\\/\\\\/g' | psql -c "copy raw_data (cid,smiles) from stdin with delimiter E'\t'" pubchem_compound
COPY 102397130 Now create the molecule table and build the index:
pubchem_compound=# alter table raw_data add primary key (cid);
ALTER TABLE
pubchem_compound=# \timing
Timing is on.
pubchem_compound=# select * into mols from (select cid,mol_from_smiles(smiles::cstring) m from raw_data order by cid limit 10000000) tmp where m is not null;
... lots of info about failing molecules deleted here
WARNING: could not create molecule from SMILES '[B+]12(CC3CC(C1)CC(C2)C3)NCC[O-]'
WARNING: could not create molecule from SMILES '[B+]12(CC3CC(C1)CC(C2)C3)NCCO'
SELECT 9999165
Time: 1722464.775 ms (28:42.465)
pubchem_compound=# create index molidx on mols using gist(m);
CREATE INDEX
Time: 3735718.147 ms (01:02:15.718)Add fingerprints and their index along with the primary keys we’ll use to link things:
pubchem_compound=# create index fps_mfp2_idx on fps using gist(mfp2);
CREATE INDEX
Time: 155112.279 ms (02:35.112)
pubchem_compound=# alter table mols add primary key (cid);
ALTER TABLE
Time: 44271.680 ms (00:44.272)
pubchem_compound=# alter table fps add primary key (cid);
ALTER TABLE
Time: 27675.441 ms (00:27.675)Now let’s do some queries.
Substructure queries
Note that all of the results below are done with a “warm” disk cache: I ran a substructure search and similarity search to try and ensure that at least parts of the indices are being cached by the operating system.
Picking the queries for a demo is always tricky, and can obviously completely slant the results one way or another, so I went to the ASAP page for J Med Chem and pulled stuff from there.
Let’s start with a query for parts of the “head” and “gate” from this paper: https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01912
pubchem_compound=# select * from mols where m@>'CC1=C(C=C(C=C1)C(N)=O)C#CC1=CN=CC=C1' limit 10;
cid | m
----------+-------------------------------------------------------------------------
11526637 | Cc1ccc(C(=O)Nc2cc(OC[C@@H]3CCCN3C)cc(C(F)(F)F)c2)cc1C#Cc1cnc2nc[nH]c2c1
(1 row)
Time: 5359.046 ms (00:05.359)Here’s the one hit:
Now the core from: https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01427
pubchem_compound=# select * from mols where m@>'CC1=CC2=C(S1)C(=O)NC(C)=N2' limit 10; cid | m
----------+---------------------------------------------------
3758971 | CCOC(=O)C1CCCn2c1nc1cc(-c3ccccc3)sc1c2=O
10622152 | COc1ccc(NC(=S)Nn2c(C)nc3cc(-c4ccccc4)sc3c2=O)cc1
1040399 | COC(=O)[C@@H]1CCc2nc3cc(-c4ccc(OC)cc4)sc3c(=O)n21
1040400 | COC(=O)[C@H]1CCc2nc3cc(-c4ccc(OC)cc4)sc3c(=O)n21
3310123 | O=c1c2sc(-c3ccccc3)cc2nc2n1CCCCC2
4376566 | CCOC(=O)C1CCCn2c1nc1cc(-c3ccc(OC)cc3)sc1c2=O
10574247 | COc1ccccc1NC(=S)Nn1c(C)nc2cc(-c3ccccc3)sc2c1=O
11404362 | Cc1cc2nc(-c3ccccc3)n(-c3ccccc3)c(=O)c2s1
10716322 | Cc1cccc(NC(=S)Nn2c(C)nc3cc(-c4ccccc4)sc3c2=O)c1
661611 | COc1ccc(-c2cc3nc4n(c(=O)c3s2)CCOC4)cc1
(10 rows)
Time: 6.532 msAnd the results:
And, finally, the core from: https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01684
pubchem_compound=# select * from mols where m@>'CN1C(=O)N(C)C2=C1C=NC(N)=N2' limit 10;
cid | m
----------+---------------------------------------------------------------------------------
10359733 | C=CCn1c(=O)n(CCCCCCCC)c2nc(N)nc(Cl)c21
10452174 | Nc1nc2c(c(=O)n1N)n(CCCl)c(=O)n2[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O
10048317 | Nc1nc2c(c(=O)n1N)n(C/C=C/c1ccccc1)c(=O)n2[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O
10454396 | [N-]=[N+]=NCC(O)Cn1c(=O)n([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c2nc(N)n(N)c(=O)c21
10476329 | Nc1nc2c(c(=O)n1N)n(Cc1ccccc1)c(=O)n2[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O
11428584 | C=CCn1c(=O)n([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c2nc(N)nc(OCC)c21
11453816 | C=CCn1c(=O)n([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c2nc(N)nc(OCN(C)C(=O)OCC)c21
9980347 | COc1ccc(Cn2c(=O)n([C@@H]3O[C@H](CO)[C@@H](O)[C@H]3O)c3nc(N)n(N)c(=O)c32)cc1
10085856 | Cn1c(=O)n2c3c1c(=O)nc(N)n3C[C@H]1O[C@@H]2[C@H](O)[C@@H]1O
9994808 | C=CCn1c(=O)n(CCCCO)c2nc(N)nc(Cl)c21
(10 rows)
Time: 107.934 msAnd the first results:
These queries are, in general, pretty quick. I’m certainly likely to spend more time looking at the results than I am waiting for them to come back.
One point it’s worth making is that queries that don’t return any results tend to be pretty fast. Here’s an example of that:
pubchem_compound=# select * from mols where m@>'O1C=NC2=C1N=C1N=CN=CC1=N2' limit 5;
cid | m
-----+---
(0 rows)
Time: 9.778 msNote that this is definitely not always true… the index does not work particularly well for queries that have massively repeating common substructures:
pubchem_compound=# select * from mols where m@>'COCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCCOCOCOCOCOCCO' limit 5;
cid | m
-----+---
(0 rows)
Time: 150521.647 ms (02:30.522)Remember what I said about about worst-case performance? There ya go.
Similarity queries
Now let’s do some similarity searches using the molecule drawn in the graphical abstract as queries. For this blog post I’m going to use the cartridge’s “neighbor” operator <%> to order the results and allow the N closest neighbors of each query molecule to be retrieved. Here’s the simple SQL function that I use for doing that:
pubchem_compound=# pubchem_compound=# create or replace function get_mfp2_neighbors2(smiles text)
returns table(cid integer, m mol, similarity double precision) as
$$
select cid,m,tanimoto_sml(morganbv_fp(mol_from_smiles($1::cstring)),mfp2) as similarity
from fps join mols using (cid)
order by morganbv_fp(mol_from_smiles($1::cstring))<%>mfp2;
$$ language sql stable ; That’s adapted from the function get_mfp2_neighbors that is in the RDKit cartridge documentation.
Let’s start with a query for the five nearest neighbors of balovaptan (https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01478):
pubchem_compound=# select * from get_mfp2_neighbors2('CN1CC2=NN=C(C3CCC(CC3)OC3=CC=CC=N3)N2C2=CC=C(Cl)C=C2C1') limit 5;
cid | m | similarity
----------+-------------------------------------------------------------+--------------------
11237434 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.7049180327868853
11237433 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1.Cl.Cl.Cl | 0.6935483870967742
10070061 | CN1CCc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.6212121212121212
11270862 | Cc1ccccc1CC(=O)N1CCC(c2nnc3n2-c2ccc(Cl)cc2CN(C)C3)CC1 | 0.5797101449275363
11442570 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)c3c[nH]c4ccccc34)CC2)C1 | 0.5774647887323944
(5 rows)
Time: 239.879 msIt’s easy (and quick) to expand that to the ten nearest neighbors:
pubchem_compound=# select * from get_mfp2_neighbors2('CN1CC2=NN=C(C3CCC(CC3)OC3=CC=CC=N3)N2C2=CC=C(Cl)C=C2C1') limit 10;
cid | m | similarity
----------+-------------------------------------------------------------+--------------------
11237434 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.7049180327868853
11237433 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1.Cl.Cl.Cl | 0.6935483870967742
10070061 | CN1CCc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.6212121212121212
11270862 | Cc1ccccc1CC(=O)N1CCC(c2nnc3n2-c2ccc(Cl)cc2CN(C)C3)CC1 | 0.5797101449275363
11442570 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)c3c[nH]c4ccccc34)CC2)C1 | 0.5774647887323944
11486399 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)CC3CC3)CC2)C1 | 0.5757575757575758
11349733 | CCCC(=O)N1CCC(c2nnc3n2-c2ccc(Cl)cc2CN(C)C3)CC1 | 0.5757575757575758
11305733 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)c3cc4ccccc4[nH]3)CC2)C1 | 0.5694444444444444
11166614 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)C3(C)CCCCC3)CC2)C1 | 0.5671641791044776
10028677 | CN1CCOC(CN2Cc3cc(Cl)ccc3-n3c(nnc3C3CCN(c4ccccn4)CC3)C2)C1 | 0.5616438356164384
(10 rows)
Time: 189.425 msHere are the first few of those, along with the similarity values:
The five nearest neighbors for AZD7648 (https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01684) aren’t particularly similar:
pubchem_compound=# select * from get_mfp2_neighbors2('CN1C(=O)N(C2CCOCC2)C2=C1C=NC(NC1=CN3N=CN=C3C=C1C)=N2') limit 5;
cid | m | similarity
----------+-----------------------------------------------------------------------+---------------------
11504903 | Cn1c(=O)c(=O)n(C2CCCC2)c2nc(Nc3ccc(C(=O)NC4CCC(N5CCOCC5)CC4)cc3)ncc21 | 0.4166666666666667
11406590 | Cn1c(=O)c(Oc2ccc(F)cc2F)cc2cnc(NC3CCOCC3)nc21 | 0.3924050632911392
10230233 | Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccc(N4CCC(CCCN5CCOCC5)CC4)cc3)ncc12 | 0.3595505617977528
9801449 | Cc1nc2cnc(Nc3ccc(N4CCOCC4)cc3)nc2n(C2CCCC2)c1=O | 0.35443037974683544
10224126 | Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccc(N4CCOCC4)c(F)c3)ncc12 | 0.3488372093023256
(5 rows)
Time: 219.653 msAs we can see:
The performance of these queries isn’t quite as good as what you can do with specialized open-source tools like chemfp, gpusimilarity, or FPSim2, but I think it’s reasonable for retrieving a set of neighbors that I’m then going to do (probably more time consuming) further analysis on.
Note that the similarity queries were considerably slower in the original version of this blog post. This was likely due to me doing a bad job of warming up the disk cache.
Some technical details:
Sizes of the tables and indices
pubchem_compound=# \d+
List of relations
Schema | Name | Type | Owner | Size | Description
--------+-----------------+----------+----------+------------+-------------
public | fps | table | glandrum | 964 MB |
public | mols | table | glandrum | 4236 MB |
public | raw_data | table | glandrum | 9091 MB |
public | raw_data_id_seq | sequence | glandrum | 8192 bytes |
(4 rows)
pubchem_compound=# \di+
List of relations
Schema | Name | Type | Owner | Table | Size | Description
--------+---------------+-------+----------+----------+---------+-------------
public | fps_mfp2_idx | index | glandrum | fps | 1226 MB |
public | fps_pkey | index | glandrum | fps | 214 MB |
public | molidx | index | glandrum | mols | 4060 MB |
public | mols_pkey | index | glandrum | mols | 214 MB |
public | raw_data_pkey | index | glandrum | raw_data | 2193 MB |
(5 rows)Performance of the substructure indices
Here are results for the substructure queries executed above. For the purposes of this analysis I removed the limit on the query:
pubchem_compound=# explain analyze select * from mols where m@>'CC1=C(C=C(C=C1)C(N)=O)C#CC1=CN=CC=C1';
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on mols (cost=2157.91..37897.03 rows=9999 width=398) (actual time=167.546..168.902 rows=1 loops=1)
Recheck Cond: (m @> 'Cc1ccc(C(N)=O)cc1C#Cc1cccnc1'::mol)
Rows Removed by Index Recheck: 328
Heap Blocks: exact=309
-> Bitmap Index Scan on molidx (cost=0.00..2155.41 rows=9999 width=0) (actual time=137.703..137.703 rows=329 loops=1)
Index Cond: (m @> 'Cc1ccc(C(N)=O)cc1C#Cc1cccnc1'::mol)
Planning Time: 0.119 ms
Execution Time: 169.178 ms
(8 rows)
pubchem_compound=# explain analyze select * from mols where m@>'CC1=CC2=C(S1)C(=O)NC(C)=N2';
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on mols (cost=2157.91..37897.03 rows=9999 width=398) (actual time=436.998..510.558 rows=46 loops=1)
Recheck Cond: (m @> 'Cc1nc2cc(C)sc2c(=O)[nH]1'::mol)
Rows Removed by Index Recheck: 14
Heap Blocks: exact=55
-> Bitmap Index Scan on molidx (cost=0.00..2155.41 rows=9999 width=0) (actual time=436.902..436.902 rows=60 loops=1)
Index Cond: (m @> 'Cc1nc2cc(C)sc2c(=O)[nH]1'::mol)
Planning Time: 0.113 ms
Execution Time: 510.801 ms
(8 rows)
pubchem_compound=# explain analyze select * from mols where m@>'CN1C(=O)N(C)C2=C1C=NC(N)=N2';
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on mols (cost=2157.91..37897.03 rows=9999 width=398) (actual time=5111.590..5113.136 rows=38 loops=1)
Recheck Cond: (m @> 'Cn1c(=O)n(C)c2nc(N)ncc21'::mol)
Rows Removed by Index Recheck: 10
Heap Blocks: exact=48
-> Bitmap Index Scan on molidx (cost=0.00..2155.41 rows=9999 width=0) (actual time=5081.767..5081.768 rows=48 loops=1)
Index Cond: (m @> 'Cn1c(=O)n(C)c2nc(N)ncc21'::mol)
Planning Time: 0.058 ms
Execution Time: 5113.296 ms
(8 rows)All of those show that the index is doing a reasonably good job of pruning compounds - the worst case query (the first one) only ends up trying at 329 substructure queries (instead of 10 million!) to find the 1 actual match.
Warming up the disk cache
There are tons of ways to do this. Here’s the approach I used for this post:
pubchem_compound=# select cid,mol_murckoscaffold(m) scaff into temporary table scaffs from mols order by cid desc limit 10;
SELECT 10
pubchem_compound=# select count(*) from mols cross join scaffs where mols.m@>scaffs.scaff;
count
--------
178828
(1 row)
pubchem_compound=# select * into blah from fps order by cid desc limit 10;
SELECT 10
pubchem_compound=# select count(*) from fps cross join blah where blah.mfp2%fps.mfp2;
count
-------
401
(1 row)That takes ~10 minutes to run for me.
Doing the queries and making the images
Since I’m not doing this one from a jupyter notebook, here’s the code snippet I’m using in jupyter to do the substructure queries and display the results:
q='CN1C(=O)N(C)C2=C1C=NC(N)=N2'
t1=time.time()
d = %sql postgresql://localhost/pubchem_compound select * from mols where m@>:q limit 10;
t2 = time.time()
print(f'{t2-t1:.2f} seconds')
ms = [Chem.MolFromSmiles(x) for x in [q]+[x[1] for x in d]]
ls = ['query']+[str(x[0]) for x in d]
Draw.MolsToGridImage(ms[:8],legends=ls,molsPerRow=4)And the equivalent thing for nearest-neighbor queries:
q='CN1CC2=NN=C(C3CCC(CC3)OC3=CC=CC=N3)N2C2=CC=C(Cl)C=C2C1'
t1=time.time()
d = %sql postgresql://localhost/pubchem_compound select * from get_mfp2_neighbors2(:q) limit 10;
t2 = time.time()
print(f'{t2-t1:.2f} seconds')
ms = [Chem.MolFromSmiles(x) for x in [q]+[x[1] for x in d]]
ls = ['query']+[f'{x[0]} {x[2] :.2f}' for x in d]
Draw.MolsToGridImage(ms[:8],legends=ls,molsPerRow=4)Both of these are using the fantastic sql-magic for jupyter from Catherine Devlin.
Mini-rant
PostgreSQL is a general purpose relational database, it lets you store collections of tables with links (relations) between them and makes it easy to do queries that combine data across tables. If you have data on a bunch of molecules that can’t be logically captured in a single table (like ChEMBL!), a relational database is a great place to put that. If you have a single table with a bunch of columns (i.e. molecules and values or fingerprints computed from them), you may want to look to another type of system. If you have a huge number of rows that you are really only interested in doing chemical queries (substructure and similarity searches) against, and you care about performance, then you almost certainly should be using a specialized system. There are plenty to choose from!