问如何利用RDKit计算微笑结构列表的分子指纹和相似性？
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Stack Overflow用户

提问于 2018-08-03 17:15:22

回答 1查看 12.6K关注 0票数 4

我用RDKit计算了两个微笑结构的分子列表之间的分子相似度。现在，我能够从两个单独的csv文件中提取微笑结构。我想知道如何将这些结构放入RDKit中的指纹模块，以及如何计算两个分子列表之间的相似性。

from rdkit import DataStructs
from rdkit.Chem.Fingerprints import FingerprintMols
ms = [Chem.MolFromSmiles('CCOC'), Chem.MolFromSmiles('CCO'), ... Chem.MolFromSmiles('COC')]
fps = [FingerprintMols.FingerprintMol(x) for x in ms]
DataStructs.FingerprintSimilarity(fps[0],fps[1])

我想把我所有的微笑结构(超过10,000)放到“女士”名单中，并得到他们的指纹。然后比较两个列表中每对分子之间的相似性，也许这里需要一个for循环？

提前感谢！

我使用熊猫数据格式来选择和打印带有结构的列表，并将列表保存到list_1和list_2中。当它运行到ms1行时，它会出现以下错误：

TypeError: No registered converter was able to produce a C++ rvalue of type std::__cxx11::basic_string<wchar_t, 
std::char_traits<wchar_t>, std::allocator<wchar_t> > from this Python object of type float

然后我检查了文件，在微笑栏里只有微笑。但是当我手动将一些分子结构放到测试列表中时，仍然存在一些错误

fpArgs['minSize'].

例如，小枝酰胺的微笑是"O=C1O-234567O=C(CN2(CCN3(CC(O-4)=O)CCN5(CC(=O6)NC)CC(=O)O-7)C1)NC"，，错误代码如下(运行fps行时)：

ArgumentError: Python argument types in
rdkit.Chem.rdmolops.RDKFingerprint(NoneType, int, int, int, int, int, float, int)
did not match C++ signature:
RDKFingerprint(RDKit::ROMol mol, unsigned int minPath=1, 
unsigned int maxPath=7, unsigned int fpSize=2048, unsigned int nBitsPerHash=2, 
bool useHs=True, double tgtDensity=0.0, unsigned int minSize=128, bool branchedPaths=True, 
bool useBondOrder=True, boost::python::api::object atomInvariants=0, boost::python::api::object fromAtoms=0, 
boost::python::api::object atomBits=None, boost::python::api::object bitInfo=None).

如果原始csv文件如下所示，如何在输出文件中包括分子名称以及相似度值：

名字，微笑，价值，value2

molecule1，CCOCN(C)(C),0.25，A

molecule2，CCO,1.12，B

molecule3，COC,2.25，C

我添加了这些代码，以便在输出文件中包含分子名称，这是一些有关名称的数组值错误(特别是对于d2)：

name_1 = df_1['id1']
name_2 = df_2['id2']
name_3 = pd.concat([name_1, name_2])
# create a list for the dataframe
d1, qu, d2, ta, sim = [], [], [], [], []
for n in range(len(fps)-1): 
    s = DataStructs.BulkTanimotoSimilarity(fps[n], fps[n+1:]) 
    #print(c_smiles[n], c_smiles[n+1:])
    for m in range(len(s)):
        qu.append(c_smiles[n])
        ta.append(c_smiles[n+1:][m])
        sim.append(s[m])
        d1.append(name_3[n])
        d2.append(name_3[n+1:][m])
    #print()
d = {'ID_1':d1, 'query':qu, 'ID_2':d2, 'target':ta, 'Similarity':sim}
df_final = pd.DataFrame(data=d)
df_final = df_final.sort_values('Similarity', ascending=False)
for index, row in df.iterrows():
    print (row["ID_1"], row["query"], row["ID_2"], row["target"], row["Similarity"])
print(df_final)
# save as csv
df_final.to_csv('RESULT_3.csv', index=False, sep=',')

python

csv

similarity

fingerprint

rdkit

回答 1

Stack Overflow用户

回答已采纳

发布于 2018-08-03 22:58:25

编辑了答案以捕捉所有评论.

RDKit具有很大的相似性功能，因此您可以将一个指纹与一个指纹列表进行比较。只需翻阅指纹清单。

如果CSV看起来像这样

第一个微笑无效的csv

smiles,value,value2
CCOCN(C)(C),0.25,A
CCO,1.12,B
COC,2.25,C

第二次正确微笑的csv

smiles,value,value2
CCOCC,0.55,D
CCCO,2.58,E
CCCCO,5.01,F

这是如何读出微笑，删除无效的，做指纹相似性没有重复和保存排序的值。

from rdkit import Chem
from rdkit import DataStructs
from rdkit.Chem.Fingerprints import FingerprintMols
import pandas as pd

# read and Conconate the csv's
df_1 = pd.read_csv('first.csv')
df_2 = pd.read_csv('second.csv')
df_3 = pd.concat([df_1, df_2])

# proof and make a list of SMILES
df_smiles = df_3['smiles']
c_smiles = []
for ds in df_smiles:
    try:
        cs = Chem.CanonSmiles(ds)
        c_smiles.append(cs)
    except:
        print('Invalid SMILES:', ds)
print()

# make a list of mols
ms = [Chem.MolFromSmiles(x) for x in c_smiles]

# make a list of fingerprints (fp)
fps = [FingerprintMols.FingerprintMol(x) for x in ms]

# the list for the dataframe
qu, ta, sim = [], [], []

# compare all fp pairwise without duplicates
for n in range(len(fps)-1): # -1 so the last fp will not be used
    s = DataStructs.BulkTanimotoSimilarity(fps[n], fps[n+1:]) # +1 compare with the next to the last fp
    print(c_smiles[n], c_smiles[n+1:]) # witch mol is compared with what group
    # collect the SMILES and values
    for m in range(len(s)):
        qu.append(c_smiles[n])
        ta.append(c_smiles[n+1:][m])
        sim.append(s[m])
print()

# build the dataframe and sort it
d = {'query':qu, 'target':ta, 'Similarity':sim}
df_final = pd.DataFrame(data=d)
df_final = df_final.sort_values('Similarity', ascending=False)
print(df_final)

# save as csv
df_final.to_csv('third.csv', index=False, sep=',')

打印出来的：

Invalid SMILES: CCOCN(C)(C)C

CCO ['COC', 'CCOCC', 'CCCO', 'CCCCO']
COC ['CCOCC', 'CCCO', 'CCCCO']
CCOCC ['CCCO', 'CCCCO']
CCCO ['CCCCO']

   query target  Similarity
9   CCCO  CCCCO    0.769231
2    CCO   CCCO    0.600000
1    CCO  CCOCC    0.500000
7  CCOCC   CCCO    0.466667
3    CCO  CCCCO    0.461538
8  CCOCC  CCCCO    0.388889
4    COC  CCOCC    0.333333
5    COC   CCCO    0.272727
0    CCO    COC    0.250000
6    COC  CCCCO    0.214286