I would like to use rdkit to generate count Morgan fingerprints and feed them to a scikit Learn model (in Python). However, I don't know how to generate the fingerprint as a numpy array. When I use
from rdkit import Chem
from rdkit.Chem import AllChem
m = Chem.MolFromSmiles('c1cccnc1C')
fp = AllChem.GetMorganFingerprint(m, 2, useCounts=True)
I get a UIntSparseIntVect that I would need to convert. The only thing I found was cDataStructs (see: http://rdkit.org/docs/source/rdkit.DataStructs.cDataStructs.html), but this does not currently support UIntSparseIntVect.
Maybe a little late to answer but these methods work for me
If you want the bits (0 and 1):
from rdkit.Chem import AllChem
from rdkit.Chem import DataStructs
mol = Chem.MolFromSmiles('c1cccnc1C')
fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024)
array = np.zeros((0, ), dtype=np.int8)
DataStructs.ConvertToNumpyArray(fp, array)
And back to a fingerprint:
bitstring = "".join(array.astype(str))
fp2 = DataStructs.cDataStructs.CreateFromBitString(bitstring)
assert list(fp.GetOnBits()) == list(fp2.GetOnBits())
If you want the counts:
fp3 = AllChem.GetHashedMorganFingerprint(mol, 2, nBits=1024)
array = np.zeros((0,), dtype=np.int8)
DataStructs.ConvertToNumpyArray(fp3, array)
print(array.nonzero())
Output:
(array([ 19, 33, 64, 131, 175, 179, 356, 378, 428, 448, 698, 707, 726,
842, 849, 889]),)
And back to a fingerprint (Not sure this is the best way to do this):
def numpy_2_fp(array):
fp = DataStructs.cDataStructs.UIntSparseIntVect(len(array))
for ix, value in enumerate(array):
fp[ix] = int(value)
return fp
fp4 = numpy_2_fp(array)
assert fp3.GetNonzeroElements() == fp4.GetNonzeroElements()
from rdkit.Chem import AllChem
m = Chem.MolFromSmiles('c1cccnc1C')
fp = AllChem.GetHashedMorganFingerprint(m, 2, nBits=1024)
fp_dict = fp.GetNonZeroElements()
arr = np.zeros((1024,))
for key, val in fp_dict.items():
arr[key] = val
It seems there is no direct way to get a numpy array so I build it from the dictionary.
