The descriptastorus provides
- fast random access to rows of properties suitable for machine learning and
- fast random access to indexed molecule files
- A mechanism for generating new descriptors for new molecules
- A mechanism for validating that you can recreate the same storage in different software/hardware environments
- An easy script for making your own descriptor files from raw data.
[n.b.] kyotocabinet is required to read/write the inchiKey and name indices This should be installed in your environment.
There are three basic ways to use DescriptaStorus:
- Make a DescriptaStore using a script
- Append new data to the store
- Use a DescriptaStore to access properties
1. install rdkit
2. install scikit-learn
pip install git+https://github.com/bp-kelley/descriptastorus
Requirements are in the setup.py file, but essentially:
- python2/3
- rdkit
- [optional but highly recommended] kyotocabinet
from descriptastorus.descriptors import rdNormalizedDescriptors
from rdkit import Chem
import logging
# make the normalized descriptor generator
generator = rdNormalizedDescriptors.RDKit2DNormalized()
generator.columns # list of tuples: (descriptor_name, numpytype) ...
# features = generator.process(smiles)
# features[0] is True/False if the smiles could be processed correcty
# features[1:] are the normalized descriptors as described in generator.columns
# example for converting a smiles string into the values
def rdkit_2d_normalized_features(smiles: str):
# n.b. the first element is true/false if the descriptors were properly computed
results = generator.process(smiles)[
processed, features = results[0], results[1:]
if processed is None:
logging.warning("Unable to process smiles %s", smiles)
# if processed is None, the features are are default values for the type
return features
see scripts/storus.py for more details:
usage: storus.py [-h] [--hasHeader] [--index-inchikey]
[--smilesColumn SMILESCOLUMN] [--nameColumn NAMECOLUMN]
[--seperator SEPERATOR]
smilesfile storage
positional arguments:
smilesfile file containing smiles strings
storage directory in which to store the descriptors
optional arguments:
-h, --help show this help message and exit
--hasHeader Indicate whether the smiles file has a header row
--index-inchikey Optionally index the descriptors with inchi keys
--smilesColumn SMILESCOLUMN
Row index (or header name if the file has a header)
for the smiles column
--nameColumn NAMECOLUMN
Row index (or header name if the file has a header)
for the name column
--seperator SEPERATOR
Row index (or header name if the file has a header)
for the name column
Example:
Suppose you have a smiles file like the following:
SMILES STRU_ID
c1ccccc1 NAME
This is a whitespace seperated file with a header. To make the standard storage and also index the inchikey:
python scripts/storus.py --smilesColumn=SMILES --nameColumn=STRU_ID --hasHeader --index-inchikey \
--seperator=" " \
smiles.txt mysmiles-store
Note that smiles files are very seperator dependent. If the smiles or name column can't be found, it is might be because the seperator is misspecified.
The default properties created are 'Morgan3Counts,RDKit2D'.
Using the descriptastore (the descriptastore is a directory of files):
from descriptastorus import DescriptaStore
d = DescriptaStore("/db/cix/descriptastorus/store")
# print out the column names
print(d.descriptors().colnames)
# this will take a while!
for moldata, descriptors in d:
smiles, name = moldata
descriptors # is a numpy array of data morgan3 counts + rdkit descriptors
Note that the descriptors may contain status flags named as "X_Calculated" where X is one of the descriptor sets, such as RDKit2D.
These are not returned by the iterator, or through the following api points:
colnames = d.getDescriptorNames()
descriptors = d.getDescriptors(index)
for moldata, descriptors in d:
...
To obtain these flags, you can either set the keepCalculatedFlags option
colnames = d.getDescriptorNames(keepCalculatedFlags=True)
descriptors = d.getDescriptors(keepCalculatedFlags=True)
or use the direct descriptor interface:
# to iterate through only the descriptors:
for descriptors in d.descriptors():
...
rows = []
for name in names:
rows.extend( d.lookupName(name) )
# sorting the rows helps with disk seeking
rows.sort()
for row in rows:
descriptors = d.getDescriptors(row)
...
rows = []
for key in inchiKeys:
rows.extend( d.lookupInchiKey(key) )
rows.sort()
for row in rows:
descriptors = d.descriptors().get(row)
smiles, name = d.molIndex().get(row)
...
The storage system is quite simple. It is made by specifying the column names and numpy types to store and also the number of rows to initialize.
Example:
>>> from descriptastorus import raw
>>> import numpy
>>> columns = [('exactmw', numpy.float64), ('numRotatableBonds', numpy.int32) ...]
>>> r = raw.MakeStore( columns, 2, "store")
>>> r.putRow(0, [45.223, 3])
After creation, to open the read only storage:
>>> r = raw.RawStore("store")
Get the number or rows:
>>> r.N
2
Get the column names:
>>> r.colnames
['exactmw', 'numRotatableBonds']
Extract the column:
>>> r.get(0)
[45.223, 3]
If the smiles file has a header
>>> from descriptastorus import MolFileIndex
>>> index = MolFileIndex.MakeSmilesIndex("data/test1.smi", "test1", hasHeader=True,
... smilesColumn="smiles", nameColumn="name")
>>> index.N
13
>>> index.getMol(12)
'c1ccccc1CCCCCCCCCCCC'
>>> index.getName(12)
13
If the smiles file has no header
>>> from descriptastorus import MolFileIndex
>>> index = MolFileIndex.MakeSmilesIndex("data/test2.smi", "test2", hasHeader=False,
... smilesColumn=1, nameColumn=0)
>>> index.N
13
>>> index.getMol(12)
'c1ccccc1CCCCCCCCCCCC'
>>> index.getName(12)
13
Using a molfile index is fairly simple:
>>> from descriptastorus import MolFileIndex
>>> idx = MolFileIndex("/db/cix/descriptastorus/test")
>>> idx.get(1000)
['CC(C)(O)c1ccc(nc1)c4ccc3C=CN(Cc2ccc(F)cc2)c3c4', 'XX-AAAA']
>>> idx.getName(1000)
'XX-AAAA'
>>> idx.getMol(1000)
CC(C)(O)c1ccc(nc1)c4ccc3C=CN(Cc2ccc(F)cc2)c3c4'
git clone https://bitbucket.org/novartisnibr/rdkit-descriptastorus.git
cd rdkit-descriptastorus
python setup.py install
TODO:
- fast forwards iteration (fast now, but could be faster)
- faster append-only store creation
- Fast molecule indexing/lookup (almost done)
- Output to bcolz pandas backend