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 oriental-cds@163.com

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fingerprint.CalculateAtomPairsFingerprint(mol)

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)

Calculate Daylight-like 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)

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)

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)

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)

Calculate E-state 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)

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)

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)

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)

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)

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)

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)

Calculate GhoseCrippen Fingerprints

fingerprint.CalculateMACCSFingerprint(mol)

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)

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>)

Calculate Pharm2D2point Fingerprints

fingerprint.CalculatePharm2D3pointFingerprint(mol, featFactory=<rdkit.Chem.rdMolChemicalFeatures.MolChemicalFeatureFactory object at 0x070D38F0>)

Calculate Pharm2D3point Fingerprints

fingerprint.CalculatePubChemFingerprint(mol)

Calculate PubChem Fingerprints

fingerprint.CalculateSimilarityPybel(fp1, fp2)

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')

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)

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.