我正在使用RDKit来计算基于具有SMILE结构的两个分子列表之间的Tanimoto系数的分子相似性。现在我可以从两个单独的csv文件中提取SMILE结构。我想知道如何将这些结构放入RDKit中的指纹模块，以及如何在两个分子列表之间逐一计算相似性？
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])
我想将我拥有的所有SMILE结构（超过10,000）放入'ms'列表并获取指纹。然后我将比较两个列表中每对分子之间的相似性，这里可能需要for循环吗？
提前致谢！
我使用pandas dataframe选择并打印出包含我的结构的列表，然后将列表保存到list_1和list_2中。当它运行到ms1行时，它有如下错误：
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，微笑列中只有SMILES。但是当我手动将一些分子结构放入列表进行测试时，仍然存在错误
fpArgs['minSize'].
例如，钆双胺的SMILES是“O = C1 [O - ] [Gd + 3] 234567 [O] = C（C [N] 2（CC [N] 3（CC（[O-] 4）= O ）CC [N] 5（CC（= [O] 6）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，如何在输出文件中包含分子名称以及相似度值：
名字，一颦一笑，值，值2
分子1，COS（C）（C），0.25，A
molecule2，CCO，1.12，B
molecule3，COC，2.25，C
我添加了这些代码以在输出文件中包含分子名称，这些是关于名称的一些数组值错误（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=',')
编辑答案以捕捉所有评论。
RDKit具有相似性的批量功能，因此您可以将一个指纹与指纹列表进行比较。只需遍历指纹列表即可。
如果CSV看起来像这样
第一个csv带有无效的SMILES
smiles,value,value2
CCOCN(C)(C),0.25,A
CCO,1.12,B
COC,2.25,C
第二个csv与正确的SMILES
smiles,value,value2
CCOCC,0.55,D
CCCO,2.58,E
CCCCO,5.01,F
这是如何读出SMILES，删除无效的，没有重复的指纹相似性，并保存排序的值。
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：
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