rdkit是一个化学信息学的python工具包，可用于计算化合物的结构相似性。

https://www.rdkit.org/docs/GettingStartedInPython.html#fingerprinting-and-molecular-similarity

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conda install -c conda-forge rdkit

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from rdkit import Chem
from rdkit.Chem import MACCSkeys
from rdkit import DataStructs
from rdkit.Chem import Draw

1、生成化合物结构指纹

1.1 Topological Fingerprints

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m = Chem.MolFromSmiles('CCOC')
# Chem.MolToSmiles(mol)
fp = Chem.RDKFingerprint(m, fpSize=1024)
# fpSize 自定义数目,默认为2048

fp.GetNumBits()
# 1024
fp.ToBitString()

image-20220618214401457

1.2 MACCS Fingerprints

长度为167的分子指纹，每一位都表示一种特定的化学结构特征

https://github.com/openbabel/openbabel/blob/master/data/MACCS.txt

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from rdkit.Chem import MACCSkeys
fp = MACCSkeys.GenMACCSKeys(m)

fp.GetNumBits()
# 167
fp.ToBitString()

image-20220618214834013

1.3 Morgan/ECFP

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from rdkit.Chem import AllChem
fp = AllChem.GetMorganFingerprintAsBitVect(m1, radius = 6, nBits = 1024)
fp.GetNumBits()
# 1024
fp.ToBitString()

image-20220822114723918

2、计算相似度

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m = Chem.MolFromSmiles('CCOC')
fp = MACCSkeys.GenMACCSKeys(m)
m2 = Chem.MolFromSmiles('CCO')
fp2 = MACCSkeys.GenMACCSKeys(m2)

DataStructs.FingerprintSimilarity(fp, fp2)
# 0.5

#默认评价相似度指标为Tanimoto
DataStructs.FingerprintSimilarity(fp, fp2, metric=DataStructs.TanimotoSimilarity)
#还有其它指标
metic_list = ['DataStructs.TanimotoSimilarity',
              'DataStructs.DiceSimilarity',
              'DataStructs.CosineSimilarity',
              'DataStructs.SokalSimilarity',
              'DataStructs.RusselSimilarity',
              'DataStructs.KulczynskiSimilarity',
              'DataStructs.McConnaugheySimilarity']
for i in metic_list:
    print(DataStructs.FingerprintSimilarity(fp, fp2, metric=eval(i)))
# 0.5
# 0.6666666666666666
# 0.6735753140545634
# 0.3333333333333333
# 0.041916167664670656
# 0.6805555555555556
# 0.3611111111111111
  • 可视化看一下
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imgs = Draw.MolsToGridImage([m,m2],molsPerRow=2,subImgSize=(200,200), legends=["mol-1","mol-2"])
imgs

image-20220618215514435