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This directory contains the scripts used to run Fraggle.
The algorithm used in the scripts was described at the 2nd RDKit UGM (October
2013). The presentation can be found at:
https://github.com/rdkit/UGM_2013/blob/master/Presentations/Hussain.Fraggle.pdf
The benchmarking carried out in the presentation utilised the open source
benchmarking platform described in:
Riniker, Sereina, and Gregory A. Landrum. "Open-source platform to benchmark
fingerprints for ligand-based virtual screening." Journal of cheminformatics 5.1
(2013): 26.
With the addition of the following scripts:
fraggle.py
cxn_tversky.py
atomcontrib.py
calculate_scored_lists_mod.py
The information below describes how to run the Fraggle similarity algorithm with
a query compound against a file of database compounds.
How to run Fraggle:
-------------------
Fraggle works in three steps:
1) Need to fragment your query molecule(s)
2) Run a Tversky Search using the generated fragments
3) Post-process results of the Tversky search to give final output
It is recommended to run a standard RDK5 similarity alongside Fraggle
The scripts requires RDKit (www.rdkit.org) be installed and properly configured.
Help is available for all the scripts using the -h option
Step 1
------
Command:
python fraggle.py <QUERY_FILE >FRAGGLE_FRAGMENTS
Exmaple command:
python fraggle.py < data/query.smi > data/query_fragmentation.csv
Format of QUERY_FILE is: SMILES ID <space or comma separated>
See query.smi for an example input file
Format of FRAGGLE_FRAGMENTS: whole mol smiles,ID,fraggle split smiles
See query_fragmentation.csv for an example output file
The following help is available using the -h option:
Program to run the first part of Fraggle. Program splits the molecule
ready for the search
USAGE: ./fraggle.py <file_of_smiles
Format of smiles file: SMILES ID (space or comma separated)
Output: whole mol smiles,ID,fraggle split smiles
Step 2
------
The second step to take the fragments generated in step 1 and run a Tversky
search against your database of molecules. This is the rate determining step of
the algorithm so it is recommended to do this against a database with an
appropriate chemistry cartridge. However a python script is provided which
utilises RDKit.
The script uses a default tversky cut-off of 0.8 (alpha=0,beta=1) which seems to
work the reasonably well for the rdk5 fp.
Command:
python rdkit_tversky.py -f FRAGGLE_FRAGMENTS <DB_SMILES_FILE >TVERSKY_OUTPUT
Example command:
python rdkit_tversky.py -f data/query_fragmentation.csv < data/ChEMBL_11265_actives.smi > data/fragmentation_tversky_out
Format of FRAGGLE_FRAGMENTS file is: whole mol smiles,ID,fraggle split smiles
See query_fragmentation.csv for an example file
Format of DB_SMILES_FILE: SMILES ID (space or comma separated)
See ChEMBL_11265_actives.smi for an example file
Format of TVERSKY_OUTPUT: query_frag_smiles,query_smiles,query_id,retrieved_smi,retrieved_id,tversky_sim
See fragmentation_tversky_out for an example file
The following help is available using the -h option:
Usage: rdkit_tversky.py [options]
Program to Tversky search results as part of Fraggle
Options:
-h, --help show this help message and exit
-f F_FILE, --frags=F_FILE
File containing the query fragmentations from Fraggle
-c CUTOFF, --cutoff=CUTOFF
Cutoff for Tversy similarity. Only Tversky results
with similarity greater than the cutoff will be
output. DEFAULT = 0.8
Format of input file: whole mol smiles,ID,fraggle split smiles
Output:
query_frag_smiles,query_smiles,query_id,retrieved_smi,retrieved_id,tversky_sim
Step 3
------
The last step is to perform the post-processing to give you the final Fraggle
similarity
Command:
python atomcontrib.py <TVERSKY_OUTPUT >FINAL_FRAGGLE_RESULTS
Example command:
python atomcontrib.py < data/fragmentation_tversky_out > data/final_fraggle_results.csv
Format of TVERSKY_OUTPUT file is:
query_frag_smiles,query_smiles,query_id,retrieved_smi,retrieved_id,tversky_sim
See fragmentation_tversky_out for an example file
Format of FINAL_FRAGGLE_RESULTS:
SMILES,ID,QuerySMI,QueryID,Fraggle_Similarity,RDK5_Similarity
See final_fraggle_results.csv for an example file
This program has several options (see help from program below):
Usage: atomcontrib.py [options]
Program to post-process Tversky search results as part of Fraggle
Options:
-h, --help show this help message and exit
-c CUTOFF, --cutoff=CUTOFF
Cutoff for fraggle similarity. Only results with
similarity greater than the cutoff will be output.
DEFAULT = 0.7
-p PFP, --pfp=PFP Cutoff for partial fp similarity. DEFAULT = 0.8
Format of input file:
query_frag_smiles,query_smiles,query_id,retrieved_smi,retrieved_id,tversky_sim
Output: SMILES,ID,QuerySMI,QueryID,Fraggle_Similarity,RDK5_Similarity
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