使用RDKit计算DataFrame分子Tanimoto系数报错，寻求解决方法

分子数据 #

{'name': ['16β-hydro-ent-kauran-17-oic acid ',
  '16α-hydro-entkauran-17-oic acid ',
  'ent-kaur-16-en-19-oic acid',
  '16β,17-dihydroxy-ent-kauran-19-oic acid ',
  'annomontacin'],
 'canonical_smile': ['CC1(CCCC2(C1CCC34C2CCC(C3)C(C4)C(=O)O)C)C',
  'CC1(CCCC2(C1CCC34C2CCC(C3)C(C4)C(=O)O)C)C',
  'CC12CCCC(C1CCC34C2CCC(C3)C(=C)C4)(C)C(=O)O',
  'CC12CCCC(C1CCC34C2CCC(C3)C(C4)(CO)O)(C)C(=O)O',
  'CCCCCCCCCCCCC(C1CCC(O1)C(CCCCCCC(CCCCCC(CC2=CC(OC2=O)C)O)O)O)O']}

报错原因 #

你调用DataStructs.BulkTanimotoSimilarity时参数类型不符合要求：该函数的设计逻辑是单个指纹对象对比一组指纹列表，但你传入了两个Pandas Series，与函数的C++签名不匹配。

修正后的代码 #

import pandas as pd
import itertools
from rdkit import Chem, DataStructs
from rdkit.Chem import PandasTools, rdFingerprintGenerator

# 初始化原始数据
raw_data = {'name': ['16β-hydro-ent-kauran-17-oic acid ',
                     '16α-hydro-entkauran-17-oic acid ',
                     'ent-kaur-16-en-19-oic acid',
                     '16β,17-dihydroxy-ent-kauran-19-oic acid ',
                     'annomontacin'],
            'canonical_smile': ['CC1(CCCC2(C1CCC34C2CCC(C3)C(C4)C(=O)O)C)C',
                                'CC1(CCCC2(C1CCC34C2CCC(C3)C(C4)C(=O)O)C)C',
                                'CC12CCCC(C1CCC34C2CCC(C3)C(=C)C4)(C)C(=O)O',
                                'CC12CCCC(C1CCC34C2CCC(C3)C(C4)(CO)O)(C)C(=O)O',
                                'CCCCCCCCCCCCC(C1CCC(O1)C(CCCCCCC(CCCCCC(CC2=CC(OC2=O)C)O)O)O)O']}
df = pd.DataFrame(raw_data)

# 生成所有两两SMILES组合
df3 = pd.DataFrame(list(itertools.combinations(df['canonical_smile'].unique(), 2)), 
                   columns=['canonical_smile1', 'canonical_smile2']).dropna()

# 添加ROMol分子对象列
PandasTools.AddMoleculeColumnToFrame(df3, 'canonical_smile1', 'ROMol1', includeFingerprints=True)
PandasTools.AddMoleculeColumnToFrame(df3, 'canonical_smile2', 'ROMol2', includeFingerprints=True)

# 初始化Morgan指纹生成器
morgan_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)

# 为每个分子生成Morgan指纹
df3["morgan1"] = df3["ROMol1"].apply(lambda x: morgan_gen.GetFingerprint(x))
df3["morgan2"] = df3["ROMol2"].apply(lambda x: morgan_gen.GetFingerprint(x))

# 逐行计算两两分子的Tanimoto系数
df3["tanimoto_morgan"] = df3.apply(lambda row: DataStructs.TanimotoSimilarity(row['morgan1'], row['morgan2']), axis=1)

# 输出结果
print(df3[['canonical_smile1', 'canonical_smile2', 'tanimoto_morgan']])

关键修改点 #

• 显式初始化Morgan指纹生成器，避免批量调用的歧义
• 使用apply逐行处理指纹对，调用DataStructs.TanimotoSimilarity()计算单个两两相似度（BulkTanimotoSimilarity适用于单个指纹对比多组指纹的场景）
• 修正原代码中不必要的缩进问题

内容的提问来源于stack exchange，提问作者jacobdavis