fingerprint module¶
fingerprint system. If you have any question please contact me via email.
2016.11.15
@author: Zhijiang Yao and Dongsheng Cao
Email: gadsby@163.com and orientalcds@163.com

fingerprint.
CalculateAtomPairsFingerprint
(mol)[source]¶ Calculate atom pairs fingerprints
Usage:
result=CalculateAtomPairsFingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateDaylightFingerprint
(mol)[source]¶ Calculate Daylightlike fingerprint or topological fingerprint
(2048 bits).
Usage:
result=CalculateDaylightFingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateECFP2Fingerprint
(mol, radius=1)[source]¶ Calculate ECFP2
Usage:
result=CalculateECFP2Fingerprint(mol)
Input: mol is a molecule object.
radius is a radius.
Output: result is a tuple form. The first is the vector of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateECFP4Fingerprint
(mol, radius=2)[source]¶ Calculate ECFP4
Usage:
result=CalculateECFP4Fingerprint(mol)
Input: mol is a molecule object.
radius is a radius.
Output: result is a tuple form. The first is the vector of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateECFP6Fingerprint
(mol, radius=3)[source]¶ Calculate ECFP6
Usage:
result=CalculateECFP6Fingerprint(mol)
Input: mol is a molecule object.
radius is a radius.
Output: result is a tuple form. The first is the vector of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateEstateFingerprint
(mol)[source]¶ Calculate Estate fingerprints (79 bits).
Usage:
result=CalculateEstateFingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateFCFP2Fingerprint
(mol, radius=1, nBits=1024)[source]¶ Calculate FCFP2
Usage:
result=CalculateFCFP2Fingerprint(mol)
Input: mol is a molecule object.
radius is a radius.
Output: result is a tuple form. The first is the vector of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateFCFP4Fingerprint
(mol, radius=2, nBits=1024)[source]¶ Calculate FCFP4
Usage:
result=CalculateFCFP4Fingerprint(mol)
Input: mol is a molecule object.
radius is a radius.
Output: result is a tuple form. The first is the vector of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateFCFP6Fingerprint
(mol, radius=3, nBits=1024)[source]¶ Calculate FCFP6
Usage:
result=CalculateFCFP4Fingerprint(mol)
Input: mol is a molecule object.
radius is a radius.
Output: result is a tuple form. The first is the vector of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateFP2Fingerprint
(mol)[source]¶ Calculate FP2 fingerprints (1024 bits).
Usage:
result=CalculateFP2Fingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateFP3Fingerprint
(mol)[source]¶ Calculate FP3 fingerprints (210 bits).
Usage:
result=CalculateFP3Fingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateFP4Fingerprint
(mol)[source]¶ Calculate FP4 fingerprints (307 bits).
Usage:
result=CalculateFP4Fingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateGhoseCrippenFingerprint
(mol, count=False)[source]¶ Calculate GhoseCrippen Fingerprints

fingerprint.
CalculateMACCSFingerprint
(mol)[source]¶ Calculate MACCS keys (166 bits).
Usage:
result=CalculateMACCSFingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculateMorganFingerprint
(mol, radius=2)[source]¶ Calculate Morgan
Usage:
result=CalculateMorganFingerprint(mol)
Input: mol is a molecule object.
radius is a radius.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.

fingerprint.
CalculatePharm2D2pointFingerprint
(mol, featFactory=<rdkit.Chem.rdMolChemicalFeatures.MolChemicalFeatureFactory object at 0x070D38F0>)[source]¶ Calculate Pharm2D2point Fingerprints

fingerprint.
CalculatePharm2D3pointFingerprint
(mol, featFactory=<rdkit.Chem.rdMolChemicalFeatures.MolChemicalFeatureFactory object at 0x070D38F0>)[source]¶ Calculate Pharm2D3point Fingerprints

fingerprint.
CalculateSimilarityPybel
(fp1, fp2)[source]¶ Calculate Tanimoto similarity between two molecules.
Usage:
result=CalculateSimilarityPybel(fp1,fp2)
Input: fp1 and fp2 are two DataStructs.
Output: result is a Tanimoto similarity value.

fingerprint.
CalculateSimilarityRdkit
(fp1, fp2, similarity='Tanimoto')[source]¶ Calculate similarity between two molecules.
Usage:
result=CalculateSimilarity(fp1,fp2) Users can choose 11 different types: Tanimoto, Dice, Cosine, Sokal, Russel, RogotGoldberg, AllBit, Kulczynski, McConnaughey, Asymmetric, BraunBlanquet Input: fp1 and fp2 are two DataStructs.
Output: result is a similarity value.

fingerprint.
CalculateTopologicalTorsionFingerprint
(mol)[source]¶ Calculate Topological Torsion Fingerprints
Usage:
result=CalculateTopologicalTorsionFingerprint(mol)
Input: mol is a molecule object.
Output: result is a tuple form. The first is the number of
fingerprints. The second is a dict form whose keys are the
position which this molecule has some substructure. The third
is the DataStructs which is used for calculating the similarity.