# Working with the command-line tools¶

The sections in this chapter describe examples of using the command-line tools to generate and work with fingerprint files.

## Generate fingerprint files from PubChem SD tags¶

In this section you’ll learn how to create a fingerprint file from an SD file which contains pre-computed CACTVS fingerprints. You do not need a chemistry toolkit for this section.

These files will be re-used in many parts of the documentation.

PubChem is a great resource of publically available chemistry information. The data is available for download (and ftp download). We’ll use some of their SD formatted files. Each record has a PubChem/CACTVS fingerprint field, which we’ll extract to generate an FPS file.

Start by downloading the files Compound_099000001_099500000.sdf.gz (from https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/Compound_099000001_099500000.sdf.gz ) and Compound_048500001_049000000.sdf.gz (from https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/Compound_048500001_049000000.sdf.gz ). At the time of writing they contain 10,740 and 14,478 records, respectively. (I chose some of the smallest files so they would be easier to open and review.)

Next, convert the files into fingerprint files. On the command line do the following two commands:

sdf2fps --pubchem Compound_099000001_099500000.sdf.gz -o pubchem_queries.fps
sdf2fps --pubchem Compound_048500001_049000000.sdf.gz -o pubchem_targets.fps


You’ll see a progress bar for each command, which looks like:

Compound_099000001_099500000.sdf.gz: 100%|█████████████| 6.77M/6.77M [00:00<00:00, 20.1Mbytes/s]

Add the –no-progress option to turn off the progess bar, as in:

sdf2fps --pubchem Compound_099000001_099500000.sdf.gz -o
pubchem_queries.fps  --no-progress


Congratulations, that was it!

If you’re curious about what an FPS file looks like, here are the first 10 lines of pubchem_queries.fps, with some of the lengthy fingerprint lines replaced with an ellipsis:

#FPS1
#num_bits=881
#type=CACTVS-E_SCREEN/1.0 extended=2
#software=CACTVS/unknown
#source=Compound_099000001_099500000.sdf.gz
#date=2022-03-01T10:58:36
07de0d00000000 ... 38d1017100000000204000000000000010200000000000000000       99000039
07de1c00020000 ... 398a405000010000000000008000000000000000000000000000       99000230
074e1c00000000 ... 0103057000000000000000000000000000000000000000000000       99001517
07de0c00000000 ... 1913097110008000000008000800400000000400000000000000       99002251


How does this work? Each PubChem record contains the precomputed CACTVS substructure keys in the PUBCHEM_CACTVS_SUBSKEYS tag. Here’s what it looks like for record 99000039, which is the first record in Compound_099000001_099500000.sdf.gz:

> <PUBCHEM_CACTVS_SUBSKEYS>
yccci4COAAAAAAQCAAAAAAAACAQAAAAAAAAAAA==


The --pubchem flag tells sdf2fps to get the value of that tag and decode it to get the fingerprint. It also adds a few metadata fields to the fingerprint file header.

The order of the FPS fingerprints are the same as the order of the corresponding record in the SDF. You can see that in the output, where 99000039 is the first record in the FPS fingerprints.

If you store records in an SD file then you almost certainly don’t use the same fingerprint encoding as PubChem. sdf2fps can decode from a number of encodings, like hex and base64. Use --help to see the list of available decoders.

The example uses -o to have sdf2fps write the output to a file instead of to stdout. By default, filenames ending in “.fps” are saved in FPS format. Use “.fps.gz” for the gzip-compressed FPS format and “.fps.zst” for the zstandard-compressed FPS format.

Filenames ending with “.fpb” are saved in FPB format. This is a binary format which is significantly faster to load.

## simsearch CSV output¶

In this section you’ll learn how to change the simsearch output format to CSV or TSV. You will need the FPS fingerprint files generated in Generate fingerprint files from PubChem SD tags but you do not need a chemistry toolkit.

The default simsearch output format is unique to chemfp, and therefore not so easy for other tools to parse directly. Use the --out option to change the output format to “csv” or “tsv” formats:

.. highlight: console

% simsearch -k 2 -q pubchem_queries.fps pubchem_targets.fps –out csv | head -8 query_id,target_id,score 99000039,48503376,0.878453 99000039,48503380,0.872928 99000230,48563034,0.858824 99000230,48731730,0.852273 99001517,48675145,0.569620 99001517,48662654,0.554217 99002251,48798046,0.810976 % simsearch -k 2 -q pubchem_queries.fps pubchem_targets.fps –out tsv | head -8 query_id target_id score 99000039 48503376 0.878453 99000039 48503380 0.872928 99000230 48563034 0.858824 99000230 48731730 0.852273 99001517 48675145 0.569620 99001517 48662654 0.554217 99002251 48798046 0.810976

These alternatives have one line for each hit, and no metadata.

NOTE: There are many variants of CSV and TSV output, especially with how to handle spaces and embedded commas and tabs. Chemfp uses the “excel” and “excel-tab” formats in Python’s “csv” module. XXX

## simsearch CSV output when no hits¶

In this section you’ll learn how to change the simsearch csv output behavior when a query has no hits. You will need the FPS fingerprint files generated in Generate fingerprint files from PubChem SD tags but you do not need a chemistry toolkit.

The CSV output format writes one output line for each query hit. What happens if a query has no hits? The previous section shows queries 99001517 and 99002251 have no hits with a threshold of 0.85, so let’s see what happens:

.. highlight: console

% simsearch –threshold 0.85 -q pubchem_queries.fps pubchem_targets.fps –out csv | head query_id,target_id,score 99000039,48732162,0.859551 99000039,48503380,0.872928 99000039,48503376,0.878453 99000039,48520532,0.854054 99000230,48563034,0.858824 99000230,48731730,0.852273 99001517,*,NaN 99002251,*,NaN 99003537,48566113,0.872449

You can see the queries with no hits get a synthetic output, by default with the target id “*” and the score of “NaN”. This makes it possible to identify queries with no hits.

Use --empty-target-id and --empty-score to change these:

% simsearch --threshold 0.85 -q pubchem_queries.fps pubchem_targets.fps  \
--out csv --empty-target-id MISSING --empty-score N/A | head
query_id,target_id,score
99000039,48732162,0.859551
99000039,48503380,0.872928
99000039,48503376,0.878453
99000039,48520532,0.854054
99000230,48563034,0.858824
99000230,48731730,0.852273
99001517,MISSING,N/A
99002251,MISSING,N/A
99003537,48566113,0.872449


Alternatively, use --no-include-empty to not generate an output line when there are not hits:

% simsearch --threshold 0.85 -q pubchem_queries.fps pubchem_targets.fps  \
query_id,target_id,score
99000039,48732162,0.859551
99000039,48503380,0.872928
99000039,48503376,0.878453
99000039,48520532,0.854054
99000230,48563034,0.858824
99000230,48731730,0.852273
99003537,48566113,0.872449
99003537,48998000,0.853535
99003537,48997697,0.898477


## NxN (self-similar) searches¶

In this section you’ll learn how to use the same fingerprints as both the queries and targets, that is, a self-similarity search. You will need the pubchem_queries.fps fingerprint file generated in Generate fingerprint files from PubChem SD tags but you do not need a chemistry toolkit.

Use the --NxN option if you want to use the same set of fingerprints as both the queries and targets. Using the pubchem_queries.fps from the previous sections:

simsearch -k 3 --threshold 0.7 --NxN pubchem_queries.fps


This code is very fast because there are so few fingerprints. For larger files the --NxN will be about twice as fast and use half as much memory compared to:

simsearch -k 3 --threshold 0.7 -q pubchem_queries.fps pubchem_queries.fps


In addition, the --NxN option excludes matching a fingerprint to itself (the diagonal term).

## Using a toolkit to process the ChEBI dataset¶

In this section you’ll learn how to create a fingerprint file from a structure file. The structure processing and fingerprint generation are done with a third-party chemisty toolkit. chemfp supports Open Babel, OpenEye, RDKit and CDK. (OpenEye users please note that you will need an OEGraphSim license to use the OpenEye-specific fingerprinters.)

We’ll work with data from ChEBI, which are “Chemical Entities of Biological Interest”. They distribute their structures in several formats, including as an SD file. For this section, download the “lite” version from https://ftp.ebi.ac.uk/pub/databases/chebi/SDF/ChEBI_lite.sdf.gz . It contains the same structure data as the complete version but many fewer tag data fields. For ChEBI 208 this file contains 146,491 records and the compressed file is 51M.

### ChEBI record titles don’t contain the id¶

Strangely, the ChEBI dataset does not use the title line of the SD file to store the record id. A simple examination shows that 68,457 of the title lines are empty, 39,478 have the title “null”, 4,262 have the title ” ” (with a single space), 1,959 have the title “ChEBI”, 56 of them are labeled “Structure #1”, and the others are usually compound names like “fluocortolone” and “bkas#30-CoA(4-)”, or identifiers like “Compound 92” or “145453870”.

(I’ve asked ChEBI to fix this, to no success after many years. Perhaps you have more influence?)

Instead, the record id is stored as value of the “ChEBI ID” tag, which looks like:

> <ChEBI ID>
CHEBI:90


By default the toolkit-based fingerprint generation tools use the title as the identifier, and print a warning and skip the record if the identifier is missing. Here’s an example with rdkit2fps

.. highlight: console

% rdkit2fps ChEBI_lite.sdf.gz ERROR: Missing title in SD record, file ‘ChEBI_lite.sdf.gz’, line 1, record #1. Skipping. ERROR: Missing title in SD record, file ‘ChEBI_lite.sdf.gz’, line 62, record #2. Skipping. ERROR: Missing title in SD record, file ‘ChEBI_lite.sdf.gz’, line 100, record #3. Skipping. ERROR: Missing title in SD record, file ‘ChEBI_lite.sdf.gz’, line 135, record #4. Skipping. … keeps on going … ERROR: Empty title in SD record after cleanup, file ‘ChEBI_lite.sdf.gz’, line 2093, record #33: first line is ‘ ‘. Skipping. … keeps on going …

These error messages come from records where no identifier could be found. The default is to report the problem to stderr, skip processing the record, and continue on to the next record. Use the --errors option to change the default behavior.

(If the first 100 records have no identifiers then the command-line tools will exit even if --errors is ignore. This is a safety mechanism. Let me know if it’s a problem.)

### The –id-tag option¶

If the identifier isn’t in the title line but is in one of the SD data items, then use --id-tag option to specify of the name of the data tag containing the id. For this data set you’ll need to write it as:

The quotes are important because of the space in the tag name.

Here’s what that looks like:

In addition to “ChEBI ID” there’s also a “ChEBI Name” tag which includes data values like “tropic acid” and “(+)-guaia-6,9-diene”. Every ChEBI record has a unique name so the names could also be used as the primary identifier instead of its id.

To use the ChEBI Name as the primary chemfp identifier, specify:

The FPS fingerprint file format allows identifiers with a space, or comma, or anything other tab, newline, and a couple of other bytes, so it’s no problem using those names directly.

### Generate fingerprints with Open Babel¶

If you have the Open Babel Python library installed then you can use ob2fps to generate fingerprints:

ob2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o ob_chebi.fps


This takes about 4 minutes on my 2019-era laptop to process all of the records, and generates messages like:

==============================
*** Open Babel Warning  in Expand
Alias R was not chemically interpreted

==============================
*** Open Babel Warning  in ReadMolecule
WARNING: Problem interpreting the valence field of an atom
The valence field specifies a valence 3 that is
less than the observed explicit valence 4.

==============================
*** Open Babel Warning  in ReadMolecule
Failed to kekulize aromatic bonds in MOL file

==============================
*** Open Babel Warning  in ReadMolecule
Invalid line: M RGP must only refer to pseudoatoms
M  RGP  2  12   1  15   2


The default generates FP2 fingerprints, so the above is the same as:

ob2fps --FP2 --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o ob_chebi.fps


ob2fps can generate several other types of fingerprints. (Use --help for a list.) For example, to generate the Open Babel implementation of the MACCS definition specify:

ob2fps --MACCS --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o chebi_maccs.fps


By default ob2fps shows a progress bar which looks like:

ChEBI_lite.sdf.gz: 90365 recs [02:21, 507.25 recs/s]


Use --no-progress to not use a progress bar.

The underlying Open Babel toolkit does not have a way to get the current location in the file, so the progress bar is only able to show the number of records processed, and not the percentage complete.

ob2fps has an alternative implementation which uses chemfp’s text toolkit to parse each record as a string, which is then passed to Open Babel. The “chemfp” implementation is able to get the current file position, letting ob2fps show a percentage complete progress bar. Use the -R option to set the “implementation” reader argument to “chemfp”, as in the following:

% ob2fps –id-tag “ChEBI ID” ChEBI_lite.sdf.gz -o ob_chebi2.fps
-R implementation=chemfp

which shows:

ChEBI_lite.sdf.gz: 58%|███████ | 31.2M/53.6M [02:41<01:41, 220kbytes/s]

The chemfp implementation takes about 5-10 seconds longer than the native Open Babel implementation.

### Generate fingerprints with OpenEye¶

If you have the OEChem Python library installed, with licenses for OEChem and OEGraphSim, then you can use oe2fps to generate fingerprints:

oe2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o oe_chebi.fps


This takes about 1 minute on my laptop and generates a number of warnings like “Stereochemistry corrected on atom number 17 of”, “Unsupported Sgroup information ignored”, and “Invalid stereochemistry specified for atom number 9 of”. Normally the record title comes after the “… of”, but the title is blank for most of the records.

As an historical note, in older ChEBI releases records CHEBI:147324 By default OEChem’s SDF reader skips empty structure records. If you really need those records, add the SuppressEmptyMolSkip flag to the default ‘flavor’ reader argument, like this:

oe2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o oe_chebi.fps \
-R flavor=Default,SuppressEmptyMolSkip


The default settings generate OEGraphSim path fingerprint with the values:

numbits=4096 minbonds=0 maxbonds=5
atype=Arom|AtmNum|Chiral|EqHalo|FCharge|HvyDeg|Hyb btype=Order|Chiral


Each of these can be changed through command-line options. Use --help for details.

oe2fps can generate several other types of fingerprints. For example, to generate the OpenEye implementation of the MACCS definition specify:

oe2fps --maccs166 --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o chebi_maccs.fps


Use --help for a list of available oe2fps fingerprints or to see more configuration details.

By default oe2fps shows a progress bar which looks like:

ChEBI_lite.sdf.gz:  24%|██      | 13.0M/53.6M [00:14<00:50, 808kbytes/s]


Use --no-progress to not use a progress bar.

### Generate fingerprints with RDKit¶

If you have the RDKit Python library installed then you can use rdkit2fps to generate fingerprints. Based on the previous examples you probably guessed that the command-line is:

rdkit2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o rdkit_chebi.fps


This takes about 8 minutes and 13 seconds on my laptop, and RDKit did not generate fingerprints for 229 of the 146,491 records. RDKit logs warning and error messages to stderr. They look like:

[14:06:39] WARNING: not removing hydrogen atom without neighbors
[14:06:39] Explicit valence for atom # 7 O, 3, is greater than permitted
[14:06:40] Explicit valence for atom # 0 He greater than permitted
[14:06:08]

****
Post-condition Violation
Violation occurred on line 91 in file /Users/dalke/ftps/rdkit-Release_2021_09_4/Code/GraphMol/PeriodicTable.h
Failed Expression: anum > -1
****



For example, RDKit is careful to check that structures make chemical sense. It rejects 3-valent oxygens and refuses to process that those structures, which is the reason for the first line of that output.

The default generates RDKit’s path fingerprints with parameters:

minPath=1 maxPath=7 fpSize=2048 nBitsPerHash=2 useHs=1


Each of those can be changed through command-line options. See rdkit2fps --help for details, where you’ll also see a list of the other available fingerprint types.

For example, to generate the RDKit implementation of the MACCS definition use:

rdkit2fps --maccs166 --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o chebi_maccs.fps


while the following generates the Morgan/circular fingerprint with radius 3:

rdkit2fps --morgan --radius 3 --id-tag "ChEBI ID" ChEBI_lite.sdf.gz


By default rdkit2fps shows a progress bar which looks like:

ChEBI_lite.sdf.gz:  52%|███   | 27.9M/53.6M [04:29<05:22, 79.8kbytes/s]


Use --no-progress to not use a progress bar.

### Generate fingerprints with CDK¶

If you have the CDK Java JAR file on your CLASSPATH and you have installed the JPype1 package (see the [installation guide for help]) then you can use cdk2fps to generate fingerprints. Based on the previous examples you can probably guess that the command-line is:

cdk2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o cdk_chebi.fps


This takes about 2 minutes and 18 seconds on my laptop, and CDK did not generate fingerprints for 17 of the 146,491 structures. CDK generated two warnings:

org.openscience.cdk.config.IsotopeFactory ERROR: Could not find major isotope for: 88
org.openscience.cdk.config.IsotopeFactory ERROR: Could not find major isotope for: 88


Chemfp lets you choose an alternate error handler (see the next section), which can help you figure out which structures could not be processed. I’ll enable the report error handler:

cdk2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o cdk_chebi.fps --errors report


This generates 17 lines of the form:

ERROR: Cannot generate fingerprint: java.lang.NullPointerException,
file 'ChEBI_lite.sdf.gz', record #8612. Skipping.


That means the record caused the CDK fingerprinter function to fail, by raising a Java NullPointerException, which chemfp catches and reports. For reference, that record is ChEBI ID CHEBI:5015.

It’s a bit tricky to figure out that record #8612 is CHEBI:5015 because the record’s initial line number isn’t shown, in turn because it isn’t available from the CDK API. cdk2fps has an alternative implementation which uses chemfp’s text toolkit to parse each record as a string, which is then passed to the CDK. The “chemfp” implementation is able to report the current line number. It is enabled with the cdk.sdf.implementation value “chemfp”, like this:

cdk2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o cdk_chebi2.fps \
--errors report -R implementation=chemfp


This adds line number information to the report:

ERROR: Cannot generate fingerprint: java.lang.NullPointerException,
file 'ChEBI_lite.sdf.gz', line 551247, record #8612. Skipping.


This variant implementation took 2 minutes and 22 seconds, so adds a small bit of overhead. Let me know if you find it useful.

The default cdk2fps fingerprint type is CDK-Daylight with parameters:

size=1024 searchDepth=7 pathLimit=42000 hashPseudoAtoms=0


Each of those can be changed through command-line options. See cdk2fps --help for details, where you’ll also see a list of the other available fingerprint types.

For example, to generate the CDK implementation of the MACCS definition use:

cdk2fps --MACCS --id-tag "ChEBI ID" ChEBI_lite.sdf.gz \
-o chebi_maccs.fps  --errors report


This generates 22 report lines of the form:

ERROR: Cannot generate fingerprint: java.lang.NullPointerException:
Aromaticity model requires implicit hydrogen count is set., file
'ChEBI_lite.sdf.gz', record #2918. Skipping.


By default cdk2fps shows a progress bar which looks like:

ChEBI_lite.sdf.gz:  25%|███        | 13.5M/53.6M [00:43<01:37, 409kbytes/s]


Use --no-progress to not use a progress bar.

## Use structures as input to simsearch¶

In this section you’ll learn how to use structures as queries to simsearch queries instead of fingerprints. You’ll need a chemistry toolkit and a fingerprint file generated from that toolkit. This section assumes you have one of the chebi_maccs.fps ChEBI fingerprint files generated in the previous section. My example will use the CDK-generated file created using:

cdk2fps --MACCS --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o chebi_maccs.fps


I’ll search the ChEBI data set for phenol with the SMILES “c1ccccc1O”. My chebi_maccs.fps starts:

The type information gives a hint of how to generate a fingerprint query for that data set, which you can do manually using cdk2fps:

#FPS1
#num_bits=166
#type=CDK-MACCS/2.0
#software=CDK/2.7.1 chemfp/4.0
#date=2022-03-01T13:57:02
00000000000000000000000000000140004480101e    phenol


I could then use a pipe to pass the cdk2fps output as input to simsearch:

% echo “c1ccccc1O phenol” | cdk2fps –MACCS | simsearch chebi_maccs.fps –threshold 1.0 #Simsearch/1 #num_bits=166 #type=Tanimoto k=all threshold=1.0 #software=chemfp/4.0 #targets=chebi_maccs.fps #target_source=ChEBI_lite.sdf.gz 1 phenol CHEBI:15882 1.00000

That’s a bit clumsy, because I have to look at the fingerprint file and figure out which command-line tools and options to use.

A simpler way is to pass the structures directly to simsearch, either as a command-line option or from an input file. In the following I’ll pass in the SMILES using the --query parameter:

% simsearch chebi_maccs.fps --threshold 1.0 --query "c1ccccc1O phenol"
#Simsearch/1
#num_bits=166
#type=Tanimoto k=all threshold=1.0
#software=chemfp/4.0
#targets=chebi_maccs.fps
#target_source=ChEBI_lite.sdf.gz
1     phenol  CHEBI:15882     1.00000


How does it work? Chemp opens the targets targets file to read the metadata section. It then uses the type string to figure out how to generate the fingerprints for that type, as well as figure out which toolkit to use for structure processing.

If the query structure is not a SMILES String then use --query-format to specify the format name. Use --query-id to specify the query id instead of using the id from the input record.

For example, the following uses the InChI for proline as the input, sets the query id to “proline”, and finds the two nearest neighbors in ChEBI:

% simsearch chebi_maccs.fps –query-format inchi –query ‘InChI=1S/C5H9NO2/c7-5(8)4-2-1-3-6-4/h4,6H,1-3H2,(H,7,8)/t4-/m0/s1’ –query-id proline -k 2 #Simsearch/1 #num_bits=166 #type=Tanimoto k=2 threshold=0.0 #software=chemfp/4.0 #targets=chebi_maccs.fps #target_source=ChEBI_lite.sdf.gz 2 proline CHEBI:17203 0.96429 CHEBI:16313 0.96429

CHEBI:17203 is “L-proline” and CHEBI:16313 is “D-proline”. I wonder why I didn’t get an exact match … but not enough to investigate.

The --query simsearch option takes the structure on the command-line. Use --queries to read queries from a file, which may be a fingerprint file or a structure file.

I’ll demonstrate with a SMILES file containing two records:

% cat simple.smi
c1ccccc1O phenol
CN1C(=O)N(C)C(=O)C(N(C)C=N2)=C12 caffeine


which I’ll use to search chebi_maccs.fps:

% simsearch --queries simple.smi chebi_maccs.fps --threshold 1.0
#Simsearch/1
#num_bits=166
#type=Tanimoto k=all threshold=1.0
#software=chemfp/4.0
#queries=simple.smi
#targets=chebi_maccs.fps
#query_source=queries.smi
#target_source=ChEBI_lite.sdf.gz
1     phenol  CHEBI:15882     1.00000
1     caffeine        CHEBI:27732     1.00000


By default simsearch uses the filename to figure out the format type and compression. Use --query-format to specify a different format. For example, if neither --query nor --queries are specified then the default reads FPS queries from stdin. I’ll use --query-format sdf.gz to have it read gzip-compressed SD records from stdin, in this case from a PubChem file:

% cat Compound_099000001_099500000.sdf.gz | \
chemfp simsearch --query-format sdf.gz chebi_maccs.fps -k 1 | head -10
#Simsearch/1
#num_bits=166
#type=Tanimoto k=1 threshold=0.0
#software=chemfp/4.0
#targets=chebi_maccs.fps
#target_source=ChEBI_lite.sdf.gz
1     99000039        CHEBI:116650    0.80000
1     99000230        CHEBI:127468    0.88372
1     99001517        CHEBI:134851    0.64583
1     99002251        CHEBI:109790    0.70909


## Make new fingerprints matching the type in an existing file¶

In this section you’ll learn how to generate fingerprints that match the fingerprint type of an existing file. You’ll need a chemistry toolkit and a fingerprint file generated from that toolkit. This section assumes you have one of the chebi_maccs.fps ChEBI fingerprint files generated in an earlier section.

In the previous section you learned how to use structures as input to simsearch; simsearch uses the target fingerprint file metadata to generate the appropriate fingerprints for each structure. What if you want to generate fingerprints appropriate for the target data set but don’t immediately want to use them for a search?

The --using FILENAME option to cdk2fps, oe2fps, rdkit2fps, and ob2fps opens the named fingerprint file to get the appropriate type. I’ll demonstrate with a simple SMILES file, where the fingerprint types comes from chebi_maccs.fps:

% cat simple.smi
c1ccccc1O phenol
CN1C(=O)N(C)C(=O)C(N(C)C=N2)=C12 caffeine

% cdk2fps simple.smi --using chebi_maccs.fps
#FPS1
#num_bits=166
#type=CDK-MACCS/2.0
#software=CDK/2.7.1 chemfp/4.0
#source=simple.smi
#date=2022-03-01T14:13:24
00000000000000000000000000000140004480101e    phenol
000000003000000001d414d90323914380f138ea1f    caffeine


How do you know to use cdk2fps instead of one of the othe programs? You don’t, but all of the chemfp programs will try to process fingerprint types from other toolkits:

% ob2fps simple.smi --using chebi_maccs.fps
WARNING: Fingerprint type 'CDK-MACCS/2.0' from the --using file 'chebi_maccs.fps' is based on the cdk toolkit.
WARNING: ob2fps expects fingerprints based on the openbabel toolkit.
#FPS1
#num_bits=166
#type=CDK-MACCS/2.0
#software=CDK/2.7.1 chemfp/4.0
#source=simple.smi
#date=2022-03-01T14:15:27
00000000000000000000000000000140004480101e    phenol
000000003000000001d414d90323914380f138ea1f    caffeine


This cross-toolkit functionality is part of the long-term chemfp design but this specific code path hasn’t yet been fully tested for all the possible error conditions, which is why it prints the two “WARNING” lines to stderr.

If you know the chemfp fingerprint type string then you could pass that in on the command-line via the --type option, as in:

% ob2fps --type "OpenBabel-ECFP2 nBits=128" simple.smi
#FPS1
#num_bits=128
#type=OpenBabel-ECFP2/1 nBits=128
#software=OpenBabel/3.1.0 chemfp/4.0
#source=simple.smi
#date=2022-03-01T14:15:55
00080020000a00000c00000000000000      phenol
000c4448000000880404000221002040      caffeine


## Alternate error handlers¶

In this section you’ll learn how to change the error handler for rdkit2fps using the --errors option.

By default the “<toolkit>2fps” programs “ignore” structures which could not be parsed into a molecule option. There are two other options. They can “report” more information about the failure case and keep on processing, or they can be “strict” and exit after reporting the error.

This is configured with the --errors option.

Here’s the rdkit2fps output using --errors report:

[12:21:03] WARNING: not removing hydrogen atom without neighbors
[12:21:03] Explicit valence for atom # 12 N, 4, is greater than permitted
ERROR: Could not parse molecule block, file 'ChEBI_lite.sdf.gz', line 24228, record #380. Skipping.
[12:21:03] Explicit valence for atom # 12 N, 4, is greater than permitted
ERROR: Could not parse molecule block, file 'ChEBI_lite.sdf.gz', line 24338, record #381. Skipping.


The first two lines come from RDKit. The third line is from chemfp, reporting which record could not be parsed. (The record starts at line 24228 of the file.) The fourth line is another RDKit error message, and the last line is another chemfp error message.

Here’s the rdkit2fps output using --errors strict:

[12:24:24] WARNING: not removing hydrogen atom without neighbors
[12:24:24] Explicit valence for atom # 12 N, 4, is greater than permitted
ERROR: Could not parse molecule block, file 'ChEBI_lite.sdf.gz', line 24228, record #380. Exiting.


Because this is strict mode, processing exits at the first failure.

The ob2fps and oe2fps tools implement the --errors option, but they aren’t as useful as rdkit2fps because the underlying APIs don’t give useful feedback to chemfp about which records failed. For example, the standard OEChem file reader automatically skips records that it cannot parse. Chemfp can’t report anything when it doesn’t know there was a failure.

The default error handler in chemfp 1.1 was “strict”. In practice this proved more annoying than useful because most people want to skip the records which could not be processed. They would then contact me asking what was wrong, or doing some pre-processing to remove the failure cases.

One of the few times when it is useful is for records which contain no identifiers. When I changed the default from “strict” to “ignore” and tried to process ChEBI, I was confused at first about why the output file was so small. Then I realized that it’s because the many records without a title were skipped, and there was no feedback about skipping those records.

I changed the code so missing identifiers are always reported, even if the error setting is “ignore”. Missing identifiers will still stop processing if the error setting is “strict”.

## chemfp’s two cross-toolkit substructure fingerprints¶

In this section you’ll learn how to generate the two substructure-based fingerprints which come as part of chemfp. These are based on cross-toolkit SMARTS pattern definitions and can be used with Open Babel, OpenEye, and RDKit. (For OpenEye users, these fingerprints use the base OEChem library but do not use the separately licensed OEGraphSim library.)

chemfp implements two platform-independent fingerprints where were originally designed for substructure filters but which are also used for similarity searches. One is based on the 166-bit MACCS implementation in RDKit and the other comes from the 881-bit PubChem/CACTVS substructure fingerprints.

The chemfp MACCS definition is called “rdmaccs” because it closely derives from the MACCS SMARTS patterns used in RDKit. (These pattern definitions are also used in Open Babel and the CDK, while OpenEye has a completely independent implementation.)

Here are example of the respective rdmaccs fingerprint for phenol using each of the toolkits.

Open Babel:

% echo "c1ccccc1O phenol" | ob2fps --in smi --rdmaccs
#FPS1
#num_bits=166
#type=RDMACCS-OpenBabel/2
#software=OpenBabel/3.1.0 chemfp/4.0
#date=2022-03-01T14:17:37
00000000000000000000000000000140004480101e    phenol


OpenEye QQQQQ:

% echo "c1ccccc1O phenol" | oe2fps --in smi --rdmaccs
#FPS1
#num_bits=166
#type=RDMACCS-OpenEye/2
#software=OEChem/2.3.0 (20191016) chemfp/3.5
#date=2021-01-27T14:46:03
00000000000000000000000000000140004480101e    phenol


RDKit:

% echo "c1ccccc1O phenol" | rdkit2fps --in smi --rdmaccs
#FPS1
#num_bits=166
#type=RDMACCS-RDKit/2
#software=RDKit/2021.09.4 chemfp/4.0
#date=2022-03-01T14:18:11
00000000000000000000000000000140004480101e    phenol


CDK:

% echo "c1ccccc1O phenol" | cdk2fps --in smi --rdmaccs
#FPS1
#num_bits=166
#type=RDMACCS-CDK/2
#software=CDK/2.7.1 chemfp/4.0
#date=2022-03-01T14:18:31
00000000000000000000000000000140004480101e    phenol


For more complex molecules it’s possible that different toolkits produce different fingerprint rdmaccs, even though the toolkits use the same SMARTS definitions. Each toolkit has a different understanding of chemistry. The most notable is the different definition of aromaticity, so the bit for “two or more aromatic rings” will be toolkit dependent.

## substruct fingerprints¶

chemp also includes a “substruct” substructure fingerprint. This is an 881 bit fingerprint derived from the PubChem/CACTVS substructure keys. They do not match the CACTVS fingerprints exactly, in part due to differences in ring perception. Some of the substruct bits will always be 0. With that caution in mind, if you want to try them out, use the --substruct option.

The term “substruct” is a horribly generic name. If you can think of a better one then let me know. Until chemfp 3.0 I said these fingerprints were “experimental”, in that I hadn’t fully validated them against PubChem/CACTVS and could not tell you the error rate. I still haven’t done that.

What’s changed is that I’ve found out over the years that people are using the substruct fingerprints, even without full validatation. That surprised me, but use is its own form of validation. I still would like to validate the fingerprints, but it’s slow, tedious work which I am not really interested in doing. Nor does it earn me any money. Plus, if the validation does lead to any changes, it’s easy to simply change the version number.

## Generate binary FPB files from a structure file¶

In this section you’ll learn how to generate an FPB file instead of an FPS file. You will need the the ChEBI file from Using a toolkit to process the ChEBI dataset and a chemistry toolkit. The FPB format was introduced with chemfp-2.0.

Note

Several chemfp features, like creating FPB files, require a valid license key. If you are using chemfp under the Base License Agreement then contact sales@dalkescientific.com to purchase a license key or request an evaluation license.

The FPB format was designed so the fingerprints can be memory-mapped directly to chemfp’s internal data structures. This makes it very fast to load, but unlike the FPS format, it’s not so easy to write with your own code. You should think of the FPB format as an binary application format, for chemfp-based tools, while the FPS format is a text-based format for data exchange between diverse programs.

The easiest way to generate an FPB file from the command line is to use the “.fpb” extension instead of “.fps” or “.fps.gz”. Here are examples using each of the toolkits.

Open Babel:

% ob2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o ob_chebi.fpb


OpenEye:

% oe2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o oe_chebi.fpb


RDKit:

% rdkit2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o rdkit_chebi.fpb


CDK:

% cdk2fps --id-tag "ChEBI ID" ChEBI_lite.sdf.gz -o cdk_chebi.fpb


The binary format isn’t human-readable. Use fpcat command-line options to see what’s inside:

% fpcat oe_chebi.fpb
#FPS1
#num_bits=4096
#type=OpenEye-Path/2 numbits=4096 minbonds=0 maxbonds=5 atype=Arom|AtmNum|Chiral|EqHalo|FCharge|HvyDeg|Hyb btype=Order|Chiral
#software=OEGraphSim/2.4.3 (20191016) chemfp/3.4
0000000 ... many zeros ...00000000000000      CHEBI:15378
0000000 ... many zeros ...00000000000000      CHEBI:16042
0000000 ... many zeros ...00000000000000      CHEBI:17792
....
182b528 ... many hex values ... a8c10c0c      CHEBI:60493


By default the fingerprints are ordered from smallest popcount to largest, which you can see in the output. A pre-ordered index is faster to search because the target popcounts are pre-computed and because it often reduces the search space.

If you want to preserve the input order then you’ll need to pipe the FPS output to fpcat and use its --preserve-order flag. See the next section for an example.

## Convert between FPS and FPB formats¶

In this section you’ll learn how to convert an FPS file into an FPB file and back, and you’ll learn how to control the fingerprint ordering. You will need the FPS files generated in Generate fingerprint files from PubChem SD tags but you do not need a chemistry toolkit. The FPB format was introduced with chemfp-2.0.

If you already have an FPS file then you can convert it directly into an FPB file, and without using a chemistry toolkit. The fpcat program converts from one format to the other.

In an earlier section I generated the files pubchem_queries.fps and pubchem_targets.fps . I’ll convert each to FPB format:

% fpcat pubchem_targets.fps -o pubchem_targets.fpb
% fpcat pubchem_queries.fps -o pubchem_queries.fpb


The FPB format is a binary format which is difficult to read directly. The easiest way to see what’s inside is to use fpcat. If you don’t specify an output filename then it sends the results to stdout in FPS format:

% fpcat pubchem_queries.fpb | head -5 | fold
#FPS1
#num_bits=881
#type=CACTVS-E_SCREEN/1.0 extended=2
#software=CACTVS/unknown
00028000e00000000000000000000000000000000000000000000000000000000000009840000000
0000c001000300000000000000000000000000000000000000000200000000000000000000000000
00000000000000000000000000000000000000000000000000000000000000        99116624


The keen-eyed reader might have noticed that the conversion does not have a “source” or “date” field. I haven’t figured out if this is a bug. Should I keep the original date and structure file source, or use the current date and FPS file source? Let me know if this is important to you.

By default when fpcat generates an FPB file it reorders the fingerprints by population count and creates a popcount index. This improves the similarity search performance, but it means that the order of the FPB file is likely different than the original FPS format. You can get a sense of this by looking at the first fingerprint in the original pubchem_queries.fps file:

% grep -v # pubchem_queries.fps  | head -1 | fold
07de0d000000000000000000000000000000000000003c060100a0010000008d2f00007800080000
0030148379203c034f13080015c0acee2a00410104ac4004101b851d261b10065f03ab8f29a41106
69001393e338d1017100000000204000000000000010200000000000000000        99000039


and confirming that it isn’t the same as the first fingerpritn in pubchem_queries.fpb.

If you want the FPB file to store the fingerprints in input order instead of the popcount order needed for optimized similarity search, then use the --preserve-order flag:

% fpcat pubchem_queries.fps --preserve-order -o input_order.fpb
% fpcat input_order.fpb | grep -v # | head -1 | fold
07de0d000000000000000000000000000000000000003c060100a0010000008d2f00007800080000
0030148379203c034f13080015c0acee2a00410104ac4004101b851d261b10065f03ab8f29a41106
69001393e338d1017100000000204000000000000010200000000000000000        99000039


On the flip side, fpcat by default preserves the input order when it creates FPS output. If you instead want to the output FPS file to be in popcount order then use the --reorder flag:

% fpcat --reorder pubchem_queries.fps | grep -v # | head -1 | fold
00028000e00000000000000000000000000000000000000000000000000000000000009840000000
0000c001000300000000000000000000000000000000000000000200000000000000000000000000
00000000000000000000000000000000000000000000000000000000000000        99116624


## Specify the fpcat output format¶

In this section you’ll learn how to specify the output format for fpcat using a command-line option instead of the filename extension. You will need the pubchem_queries.fpb file from Generate fingerprint files from PubChem SD tags.

If you do not specify an output filename then fpcat will output the fingerprints in FPS format to stdout. If you specify a filename then by default it will look at the extension to determine if the output should be an FPB (“.fpb”), FPS (“.fps”), or gzip or Zstandard compressed FPS (“.fps.gz” or “.fps.zst”) file. The FPS format is used for unrecognized extensions.

In a few rare cases you may want to use a format which doesn’t match the default. To be honest, the examples I can think of aren’t that realistic, but let’s suppose you want to output the contents of an FPB file to stdout in gzip’ed FPS format, and count the number of bytes in compressed output. I’ll use the use the –out flag to change the format to ‘fps.gz’ from the default of ‘fps’, then compare the resulting size with the uncompressed form:

% fpcat pubchem_queries.fpb --out fps | wc -c
2511714
% fpcat pubchem_queries.fpb --out fps.gz | wc -c
314393


It’s not that useful because you could pipe the uncompressed output to gzip, which is also likely faster:

% fpcat pubchem_queries.fpb --out fps | gzip -c -9 | wc -c
11921


In case you’re wondering, chemfp 3.4 added support for zstandard compression, if the “zstandard” Python module is available.

% fpcat pubchem_queries.fpb –out fps.zst | wc -c 293806

Chemfp cannot write an FPB file to stdout. In fact, the output file must be seek-able, which means it can’t be a named pipe either.

## Alternate fingerprint file formats¶

In this section you’ll learn about chemfp’s support for other fingerprint file formats.

Chemfp started as a way to promote the FPS file format for fingerprint exchange. Chemfp 2.0 added the FPB format, which is a binary format designed around chemfp’s internal search data structure so it can be loaded quickly.

There are many other fingerprint formats. Perhaps the best known is the Open Babel FastSearch format. Two others are Dave Cosgrove’s flush format, and OpenEye’s “fpbin” format.

The chemfp_converters package contains utilities to convert between the chemfp formats and these other formats.:

# Convert from/to Dave Cosgrove Flush format
flush2fps drugs.flush
fps2flush drugs.fps -o drugs.flush

# Convert from/to OpenEye's fpbin format
fpbin2fps drugs.fpbin --moldb drugs.sdf
fps2fpbin drugs_openeye_path.fps --moldb drugs.sdf -o drugs.fpbin

# Convert from/to Open Babel's FastSearch format
fs2fps drugs.fs --datafile drugs.sdf
fps2fs drugs_openbabel_FP2.fps  --datafile drugs.sdf  -o drugs.fs


Of the three formats, the flush format is closest to the FPS data model. That is, it stores fingerprint records as an identifier and the fingerprint bytes. By comparison, the FastSearch and fpbin formats store the fingerprint bytes and an index into another file containing the structure and identifier. It’s impossible for chemfp to get the data it needs without reading both files.

Chemfp has special support for the flush format. If chemfp_converters is installed, chemfp will use it to read and write flush files nearly everywhere that it accepts FPS files. You can use it at the output to oe2fps, rdkit2fps, and ob2fps, and as the input queries to simsearch, and as both input and output to fpcat. (You cannot use it as the simsearch targets because that code has been optimized for FPS and FPB search, and I haven’t spent the time to optimize flush file support.)

This means that if chemfp_converters is installed then you can use fpcat to convert between FPS, FPB, and and flush file formats. For examples:

fpcat drugs.flush -o drugs.fps
fpcat drugs.fps -o drugs.flush


In addition, you can use it at the API level in chemfp.open(), chemfp.load_fingerprints(), chemfp.open_fingerprint_writer(), and FingerprintArena.save().

Note that the flush format does not support the FPS metadata fields, like the fingerprint type, and it only support fingerprints which are a multiple of 32 bits long. Also, compressed flush files are not supported.

## The FPB format¶

In this section you’ll learn about the FPB format.

The FPS format is a human-readable text format. It’s meant to be easy to create software to read or write FPS files so it can be used as a way to exchange fingerprint data between diffferent programs. The downside is it’s relatively slow to process. Chemfp can search about 1M 2048-bit FPS fingerprints in one second, or load about 250K 2048-bit fingerprints/second into memory in the same time.

The FPB format is a binary format which is much faster to load, though internally more complex. It can search 1M fingerprints in about 10 millisecond, and the load time mostly depends on the file system performance.

This makes a big difference in web development, where the web app might restart every time a file is edited, and in command-line tools, where the load time might be far greater than the analysis time.

Note though that the Base License Agreement does not permit people to create FPB files, and the chemfp binary distributions contain a license manager which restricts access to that feature without an authorized license key. See the chemfp licensing page for licensing options and for how to request a license key and/or quote for license.

Internally the FPB file contains an 8-byte signature followed by a set of chunks. Each chunk contains an 8-byte length field followed a 4-byte identifier followed by n bytes of data, where n is the value of the length field. Those familiar with PNG or other FourCC format will find this familiar.

Different chucks contain different types of data. For example, the “META” chunk contains the metadata, and the “AREN” chuck contains the fingerprint data, organized in a way that makes them easy to load into a chemfp arena, hence the name.

While the Base License Agreement does not permit people to create an FPB file, it does allow people to load an FPB file, including FPB files generated by third-party tools.

However, the Base License Agreement also does not generally permit in-memory searches of fingerprint data sets with more than 50,000 fingerprints. If you try you’ll get a message like the following:

A valid chemfp license key is required to search an arena with more than 50,000 fingerprints.
Email sales@dalkescientific.com to purchase a license key or request a demo license key.
Cannot run simsearch. Exiting.


The exception is if the FPB file is a licensed FPB file.

A licensed FPB file has an embedded license key (stored in the “CFPL” chunk) which, if valid, unlocks all of the license manager restrictions for using that file.

## Get licensed FPB files containing ChEMBL 29 fingerprints¶

In this section you’ll learn how to get the ChEMBL 29 fingerprints as a chemfp licensed FPB file. For background you should read the previous section on The FPB format <fpb_format>.

curl -O https://chemfp.com/datasets/chembl_29.fpb.gz gunzip chembl_29.fpb.gz

If curl isn’t installed, try:

wget https://chemfp.com/datasets/chembl_29.fpb.gz


The result is the licensed FPB file chemfp_29.fpb.

Note

Long version: The ChEMBL 29 distribution includes pre-computed RDKit Morgan2 fingerprints in FPS format. These can be searched directly, like:

% simsearch --query c1ccccc1CN -k 2 chembl_29.fps.gz --times
#Simsearch/1
#num_bits=2048
#type=Tanimoto k=2 threshold=0.0
#software=chemfp/4.0
#targets=chembl_29.fps.gz
#target_source=chembl_29.fps.gz
2     Query1  CHEMBL522       1.0000000       CHEMBL14186     0.9333333
open 0.01 read 0.00 search 3.85 output 0.00 total 3.60


or decompressed first, using “gunzip”, to skip the decompression overhead:

% gunzip chembl_29.fps.gz
% simsearch --query c1ccccc1CN -k 2 chembl_29.fps --times
... lines removed for clarity ...
2     Query1  CHEMBL522       1.0000000       CHEMBL14186     0.9333333
open 0.02 read 0.00 search 2.15 output 0.00 total 1.87


The chemfp project also distributes ChEMBL fingerprints which have been reformatted to the FPB format. Many of the examples in the documentation will use chembl_29.fpb. Here’s how to get that file.

Step 2: Decompress it with:

gunzip chembl_29.fpb.gz


Step 3: (optional) View the license terms:

chemfp fpb_text chembl_29.fpb.gz


## Similarity search with the FPB format¶

In this section you’ll learn how to do a similarity search using an FPB file as the target. You will need ChEMBL 29 as a chemfp-licensed FPB file and you will need the RDKit chemistry toolkit.

The file chembl_29.fpb contains the ChEMBL-generated RDKit Morgan circular fingerprints for ChEMBL 29, reformatted in FPB format, and containing a license key which unlocks restrictions on using chemfp to work with that file.

All of the chemfp tools support FPB files as input or output formats, including simsearch. Here’s an example using first FPS format then FPB format (run three times each, reporting only the fastest time:

% time simsearch --query c1ccccc1CN -k 2 chembl_29.fps > /dev/null
2.508u 0.426s 0:03.25 89.8%   0+0k 0+0io 0pf+0w

% time simsearch --query c1ccccc1CN -k 2 chembl_29.fpb > /dev/null
0.648u 0.175s 0:00.83 97.5%   0+0k 0+0io 0pf+0w


These times are reported as “user time” (the CPU time spent by the program), “system time” (the time spent by the operating system, in this case, to read and transfer data from disk), and “wall clock time”, which is the overall elapsed time. In this cae, it took 3.25 second to search the FPS file and 0.83 second to search the FPB file.

That’s a factor of 4, which is pretty good, but perhaps not that impressive.

### Performance breakdown¶

That’s because the total time includes the time needed to load RDKit, parse the query SMILES, and generate the query fingerprint. To start, it takes 0.04 seconds for Python to start working:

% time python -c 'pass'
0.027u 0.013s 0:00.04 75.0%   0+0k 0+0io 0pf+0w


% time python -c 'from rdkit.Chem import rdMolDescriptors'
0.376u 0.081s 0:00.28 160.7%  0+0k 0+0io 0pf+0w


Finally, chemfp’s wrapper to the RDKit toolkit adds another 0.2 seconds:

% time python -c 'from chemfp import rdkit_toolkit, rdkit_types'
0.522u 0.123s 0:00.47 136.1%  0+0k 0+0io 0pf+0w


Note

The times depend very much on your operating system, the location and type of the file system, the file cache, and more.

To demonstrate this overhead, I’ll pre-compute the fingerprints so chemfp doesn’t need to import RDKit:

% cat benzylamine.smi
c1ccccc1CN benzylamine
% rdkit2fps benzylamine.smi -o benzylamine.fps
--using chembl_29.fpb --no-progress
% tail -1 benzylamine.fps | fold -w 70
0000000000000000000001000000000000000000000000000000000000000000000000
0000000000000000000000000020000000000000000000000000000000000000000000
0000000000000000000000000000000000000000000000000000000000000000000000
0000000000000000000000000000000000200000000000008000000004000001000000
0000000000000800008000000000000000000000000000000000000000000000100000
0000000000000000000000000000000000000000000000000000000000000000000000
0000000000000000500400000000000000000000000000000200002000000000000000
0000000000000000000000        benzylamine


then use the fingerprint file as input:

% time simsearch --queries benzylamine.fps -k 2 chembl_29.fpb > /dev/null
0.245u 0.084s 0:00.38 84.2%   0+0k 0+0io 0pf+0w


The 0.47 seconds of startup time, plus the 0.38 seconds of search time, minus the doubly-counted Python startup time of 0.04 seconds, is 0.81 seconds, which is quite close to the time of 0.83 seconds I measured earlier.

To give a direct comparison, I’ll use the same query file to search the FPS file:

% time simsearch -q benzylamine.fps -k 2 chembl_29.fps > /dev/null
1.810u 0.300s 0:02.42 87.1%   0+0k 0+0io 0pf+0w


FPB search is now about 6 times faster than FPS search. And of that 0.38 seconds, 0.08 seconds or 20% is in file I/O.

## Converting large data sets to FPB format¶

In this section you’ll learn how to generate an FPB file on computers with relatively limited memory. To be realistic, this example uses the complete PubChem data set, and extracts the CACTVS/PubChem fingerprints which are in each record. You do not need a chemistry toolkit for this section.

The most direct way to extract the PubChem fingerprints from a PubChem distribution is to use sdf2fps:

sdf2fps --pubchem pubchem/Compound_*.sdf.gz -o pubchem.fpb


This uses the default FPB writer options, which stores all of the fingerprints in memory, sorts them, and saves the result to the output file. This may use several times as much memory as the final FPB output size, which is a bit unfortunate if you want to generate a 7 GB FPB file on a 12 GB machine.

When I updated this section in June 2020, it took around 25GB of memory to create an FPB file with 102,768,482 PubChem fingerprints, and the final file was about 14GB.

(Note: see the next section for a two-stage solution that lets you parallelize fingerprint generation.)

The “*2fps” command-line tools do not have a way to change the default writer options, although fpcat does. The --max-spool-size option sets a rough upper bound to the amount of memory to use. When enabled, the writer breaks the input into parts and creates a temporary FPB file for each part. At the end, it merges the sorted data from the temporary FPB files to get the final FPB file. Be aware that the specified spool size is only approximate and is not a hard limit on the maximum amount of memory to use. You may need to experiment a bit if you have tight constraints, and this option might not be as useful as I thought it was.

The value must be a size in bytes, though suffixes like M or MB for megabyte and T or TB for terabyte are also allowed. These are in base-10 units, so 1 MB = 1,000,000 bytes. Spaces are not allowed between the number and the suffix, so “200MB” is okay but “200 MB” is not. The size must be at least 20 MB.

Here is an example of how to convert the CACTVS fingerprints from all of PubChem to an FPB file, using a relatively small limit of 200 MB:

sdf2fps --pubchem pubchem/Compound_*.sdf.gz | fpcat --max-spool-size 200MB -o pubchem.fpb


This will take a while! The sdf2fps alone takes almost 45 minutes on a ca. 2017-era Haswell machine.

If I save the intermediate results to an FPS file then the in-memory fpcat conversion from FPS to FPB takes 5½ minutes and requires 25GB of memory.

With spool of 200MB, the conversion takes nearly 10 minutes. According to htop, the spooled conversion required, near the peak, 13.3G of virtual memory, a resident set size of 12G, and 10.6G of shared shared pages. The shared pages are from memory-mapping the intermediate FPB files, so this probably required only 2GB of real memory.

If I use a 1GB spool size, the conversion time decreases from 10 to 8 minutes, and uses about the same amount of peak memory.

The temporary files will be placed under the appropriate temporary directory for your operating system. If that disk isn’t large enough for the intermediate files then use the --tmpdir option of fpcat to specify an alternate directory:

fpcat --max-spool-size 1GB pubchem.fps -o pubchem.fpb --tmpdir /usr/tmp


Another option is to specify the directory location using the TMPDIR, TEMP, or TMP environment variables, which are resolved in that order. The details are described in the Python documentation for tempfile.tempdir.

## Faster gzip decompression¶

In this section you’ll learn how to use an external program to improve the performance of reading gzip files. You will need PubChem file Compound_016000001_016500000.sdf.gz.

The PubChem file Compound_016000001_016500000.sdf.gz is one of the largest files from PubChem, measured by compressed size. The copy I have is 543M compressed and 3.9G uncompressed.

It takes about 17 seconds to extract the PubChem fingerprints from the compressed file:

% sdf2fps –pubchem Compound_016000001_016500000.sdf.gz -o pubchem.fps Compound_016000001_016500000.sdf.gz: 100%|████████████| 569M/569M [00:17<00:00, 32.4Mbytes/s]

In comparison, it takes about 10 seconds to extract the fingerprints from the uncompressed file:

% sdf2fps –pubchem Compound_016000001_016500000.sdf -o pubchem.fps Compound_016000001_016500000.sdf: 100%|████████████████| 4.19G/4.19G [00:09<00:00, 434Mbytes/s]

There are two sources for the overhead. First, a fair amount of chemfp’s internal gzip processing uses Python. Second, it’s single-threaded, that is, a block of text is decompressed, then passes to the FPS parser.

Chemfp has the option to run an external program to handle decompression. Use the environment “CHEMFP_GZCAT” to specify the program to use. If called with no arguments, it must read from stdin. If called with one argument, it must read from the named file. The decompressed data must be written to stdout, so it can be read by chemfp.

In most cases you’ll use “zcat” (for Linux-based OSes) or “gzcat” (for macOS).

Here’s an example:

% env CHEMFP_GZCAT=gzcat sdf2fps --pubchem \
Compound_016000001_016500000.sdf.gz -o pubchem.fps
Compound_016000001_016500000.sdf.gz: 442852 recs [00:15, 28125.27 recs/s]


(The progress bar can’t give a progress bar because chemfp doesn’t know the current read position relative to the entire file.)

An improvement of two seconds may not sound like much, but 10% performance gain is nice when processing many gzip-compressed files.

## Generate fingerprints in parallel and merge to FPB format¶

In this section you’ll learn how to merge multiple sorted fingerprints into a single FPB file.

The previous section used a single shell command to extract the PubChem/CACTVS fingerprints from PubChem and generate an FPB file. This is easy to write and understand, but more complex versions may be more appropriate.

For one, I have four cores on my desktop computer, and I want to use them to process the PubChem files in parallel. The previous section was only single threaded.

I have all my PubChem files in ~/pubchem/. For each “Compound_*.sdf.gz” file in that directory I want to extract the CACTVS/PubChem fingerprints and create an intermediate FPS file in the local directory. That’s equivalent to running the following commands:

sdf2fps --pubchem ~/pubchem/Compound_000000001_000500000.sdf.gz \\
-o Compound_000000001_000500000.fps
sdf2fps --pubchem ~/pubchem/Compound_000500001_001000000.sdf.gz \\
-o Compound_000500001_001000000.fps
... 291 more lines ...


except that I want to run four at a time.

This is what GNU Parallel was designed for. It’s a command-line tool which can parallelize the execution of other command-lines.

I’ll start by explaining the core command-line substitution pattern:

sdf2fps --pubchem {} -o {/..}.fps'


The {} will be replaced with a filename, and {/..} will be replaced with the base filename, without the directory path prefix or the two suffixes. That is, when {} is “/Users/dalke/pubchem/Compound_000000001_000500000.sdf.gz” then {/..} will be “Compound_000000001_000500000.fps”.

Since I want to generate an FPS file, I added the “.fps” as a suffix to the second substitution parameter.

I then tell GNU parallel which command-line to use, along with a few other parameters. Here’s the full line, which I split over two lines to make it more readable:

parallel --plus --no-notice --bar 'sdf2fps --pubchem {}
-o {/..}.fps' ::: ~/pubchem/Compound_*.sdf.gz


The --plus tells GNU parallel to recognize an expanded set of replacement strings. (“{/..}” is not part of the standard set of patterns.)

The --no-notice tells it to not display the message about citing GNU parallel in scientific papers.

The --bar enables a progress bar, which looks like this:

30% 88:205=11m17s /Users/dalke/pubchem/Compound_045500001_046000000.sdf.gz


This status line shows that processing is 30% complete, which is file 88 out of 205, and there’s an estimated 11 minutes and 17 seconds remaining.

Finally, the “:::” indicates that the remaining options are the list of parameters to pass to the command-line template for parallelization.

After about 21 minutes, using 4 CPUs on my laptop (with an effective scaling of 2.8), I now have a large number of FPS files, which I want to merge into a single FPB file. I’ll use fpcat:

fpcat --max-spool-size 1GB Compound*.fps -o pubchem.fpb


Unfortunately my laptop ran out of disk space, so I’ll just leave it a that; re-doing the same command on a server machine won’t provide you any new information.