Difference between revisions of "Tutorial:Fingerprints"

Latest revision as of 01:44, 23 April 2010

Molecular fingerprints encode molecular structure in a series of binary digits (bits) that represent the presence or absence of particular substructures in the molecule. Comparing fingerprints will allow you to determine the similarity between two molecules, search databases, etc., but does not include full structural data (such as coordinates).

Available fingerprints

You can see the available fingerprints by typing the following command:

PROMPT> babel -L fingerprints
FP2    Indexes linear fragments up to 7 atoms.
FP3    SMARTS patterns specified in the file patterns.txt
FP4    SMARTS patterns specified in the file SMARTS_InteLigand.txt
MACCS    SMARTS patterns specified in the file MACCS.txt

At present there are four types of fingerprints: FP2, a path-based fingerprint which indexes small molecule fragments (somewhat similar to the Daylight fingerprints), fingerprint types FP3 and FP4 which both use a series of SMARTS queries that are stored in patterns.txt and SMARTS_InteLigand.txt, and a MACCS fingerprint that uses the SMARTS pattersn in MACCS.txt.

Note that you can tailor these fingerprints to your own needs by adding your own SMARTS queries to these files. On UNIX and Mac systems, these files are frequently found in /usr/local/share/openbabel under a directory for each version of Open Babel.

Similarity searching

For relatively small datasets (<10,000's) it is possible to do similarity searches without the need to build a similarity index, however larger datasets (up to 100,000's) can be searched rapidly once a fastsearch index has been built.

Small datasets

On small datasets these fingerprints can be used in a variety of ways. The following command gives you the Tanimoto coefficient between a SMILES string in mysmiles.smi and all the molecules in mymols.sdf:

PROMPT>  babel  mysmiles.smi  mymols.sdf -ofpt
MOL_00000067   Tanimoto from first mol = 0.0888889
MOL_00000083   Tanimoto from first mol = 0.0869565
MOL_00000105   Tanimoto from first mol = 0.0888889
MOL_00000296   Tanimoto from first mol = 0.0714286
MOL_00000320   Tanimoto from first mol = 0.0888889
MOL_00000328   Tanimoto from first mol = 0.0851064
MOL_00000338   Tanimoto from first mol = 0.0869565
MOL_00000354   Tanimoto from first mol = 0.0888889
MOL_00000378   Tanimoto from first mol = 0.0816327
MOL_00000391   Tanimoto from first mol = 0.0816327
11 molecules converted

The default fingerprint used is the FP2 fingerprint. You change the fingerprint using the "f" output option as follows:

PROMPT>  babel mymols.sdf -ofpt -xfFP3

The "-s" option of babel is used to filter by SMARTS string (see Babel). If you wanted to know the similarity only to the substituted bromobenzenes in mymols.sdf then you might combine commands like this (note: if the query molecule does not match the SMARTS string this will not work as expected, as the first molecule in the database that matches the SMARTS string will instead be used as the query):

PROMPT>  babel mysmiles.smi mymols.sdf -ofpt -s c1ccccc1Br
MOL_00000067   Tanimoto from first mol = 0.0888889
MOL_00000083   Tanimoto from first mol = 0.0869565
MOL_00000105   Tanimoto from first mol = 0.0888889

If you don't specify a query file, babel will just use the first molecule in the database as the query:

PROMPT>  babel mymols.sdf  -ofpt

MOL_00000067
MOL_00000083   Tanimoto from MOL_00000067 = 0.810811
MOL_00000105   Tanimoto from MOL_00000067 = 0.833333
MOL_00000296   Tanimoto from MOL_00000067 = 0.425926
MOL_00000320   Tanimoto from MOL_00000067 = 0.534884
MOL_00000328   Tanimoto from MOL_00000067 = 0.511111
MOL_00000338   Tanimoto from MOL_00000067 = 0.522727
MOL_00000354   Tanimoto from MOL_00000067 = 0.534884
MOL_00000378   Tanimoto from MOL_00000067 = 0.489362
MOL_00000391   Tanimoto from MOL_00000067 = 0.489362
10 molecules converted 

Large datasets

On larger datasets it is necessary to first build a fastsearch index. This is an new file that stores a database of fingerprints for the files indexed. You will still need to keep both the new .fs fastsearch index and the original files. However, the new index will allow significantly faster searching and similarity comparisons. The index is created with the following command:

PROMPT>  babel mymols.sdf -ofs

This builds mymols.fs with the default fingerprint (unfolded). The following command uses the index to find the 5 most similar molecules to the molecule in query.mol:

PROMPT>  babel mymols.fs results.sdf -Squery.mol -at5

or to get the matches with Tanimoto>0.6 to 1,2-dicyanobenzene:

PROMPT>  babel mymols.fs results.sdf -sN#Cc1ccccc1C#N -at0.6

Substructure searching

Small datasets

This command will find all molecules containing 1,2-dicyanobenzene and return the results as SMILES strings:

PROMPT>  babel mymols.sdf -sN#Cc1ccccc1C#N results.smi

If all you want output are the molecule names then adding -xt will return just the molecule names.

PROMPT>  babel mymols.sdf -sN#Cc1ccccc1C#N results.smi -xt

Large datasets

First of all, you need to create a fastsearch index (see above). The index is created with the following command:

PROMPT>  babel mymols.sdf -ofs

Substructure searching is as for small datasets, except that the fastsearch index is used instead of the original file. This command will find all molecules containing 1,2-dicyanobenzene and return the results as SMILES strings:

PROMPT>  babel mymols.fs -ifs -sN#Cc1ccccc1C#N results.smi

If all you want output are the molecule names then adding -xt will return just the molecule names.

PROMPT>  babel mymols.fs -ifs -sN#Cc1ccccc1C#N results.smi -xt

Case study: Search ChEMBLdb

This case study uses a combination of the techniques described above for similarity searching using large databases and using small databases. Note that we are using the default fingerprint for all of these analyses. The default fingerprint is FP2, a path-based fingerprint (somewhat similar to the Daylight fingerprints).

(1) Download Version 2 of ChEMBLdb from ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/.

(2) After unzipping it, make a fastsearch index (this took 18 minutes on my machine, for the 500K+ molecules)

babel chembl_02.sdf -ofs

(3) Let's use the first molecule in the sdf file as a query. Using Notepad (or on Linux, "head -79 chembl_02.sdf") extract the first molecule and save it as "first.sdf". Note that the molecules in the ChEMBL sdf do not have titles; instead, their IDs are stored in the "chebi_id" property field.

(4) This first molecule is 100183. Check its ChEMBL page. It's pretty weird, but is there anything similiar in ChEMBLdb? Let's find the 5 most similar molecules:

babel chembl_02.fs mostsim.sdf -Sfirst.sdf -at5

(5) The results are stored in mostsim.sdf, but how similar are these molecules to the query?

babel first.sdf mostsim.sdf -ofpt
>
>   Tanimoto from first mol = 1
Possible superstructure of first mol
>   Tanimoto from first mol = 0.986301
>   Tanimoto from first mol = 0.924051
Possible superstructure of first mol
>   Tanimoto from first mol = 0.869048
Possible superstructure of first mol
>   Tanimoto from first mol = 0.857143
6 molecules converted
76 audit log messages

(6) That's all very well, but it would be nice to show the ChEBI IDs. Let's set the title field of mostsim.sdf to the content of the "chebi_id" property field, and repeat step 5.

babel mostsim.sdf mostsim_withtitle.sdf --append "chebi_id"
babel first.sdf mostsim_withtitle.sdf -ofpt
>
>100183   Tanimoto from first mol = 1
Possible superstructure of first mol
>124893   Tanimoto from first mol = 0.986301
>206983   Tanimoto from first mol = 0.924051
Possible superstructure of first mol
>207022   Tanimoto from first mol = 0.869048
Possible superstructure of first mol
>607087   Tanimoto from first mol = 0.857143
6 molecules converted
76 audit log messages

(7) Here are the ChEMBL pages for these molecules: 100183, 124893, 206983, 207022, 607087. I think it is fair to say that they are pretty similiar. In particular, the output states that 206983 and 207022 are possible superstructures of the query molecule, and that is indeed true.