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我正在尝试建立一个模型，该模型可以根据其微笑字符串表示来预测分子的 caco-2 系数。我的解决方案是基于这个例子。由于我需要预测实际值，因此我使用RandomForestRegressor. 将一些分子手动添加到代码中，一切正常（尽管预测本身非常错误）：

from rdkit import Chem, DataStructs     #all the nice chemical stuff, ConvertToNumpyArray
from rdkit.Chem import AllChem
from sklearn.ensemble import RandomForestRegressor      #our regressor
from sklearn.model_selection import train_test_split    
import numpy as np



# generate molecules
m1 = Chem.MolFromSmiles('Cc1ccc(NNC(=O)c2ccc(CN3C(=O)CCC3=O)cc2)cc1Cl')
m2 = Chem.MolFromSmiles('Nc1ccc(C(=O)N2CCN(c3cc[nH+]cc3)CC2)cc1[N+](=O)[O-]')
m3 = Chem.MolFromSmiles('CN(Cc1[nH+]ccn1C)C(=O)CCc1ccsc1')
m4 = Chem.MolFromSmiles('COc1ccc([N+](=O)[O-])cc1C(=O)NCCC[NH+]1CCCC1')
m5 = Chem.MolFromSmiles('C[NH+]1CCN(S(=O)(=O)c2ccc(NC(=O)Cc3ccc([N+](=O)[O-])cc3)cc2)CC1')
m6 = Chem.MolFromSmiles('CCc1ccc(S(=O)(=O)Nc2ccc(NC(C)=O)cc2)cc1')
m7 = Chem.MolFromSmiles('O=C(COC(=O)c1ccc(S(=O)(=O)N2CCCCC2)cc1)c1ccc(F)cc1')
m8 = Chem.MolFromSmiles('COC(=O)c1ccc(S(=O)(=O)NCc2csc3ccc(Cl)cc23)n1C')
m9 = Chem.MolFromSmiles('CCC(C)N1C(=O)C(=CNc2ccccc2C(=O)[O-])C(=O)NC1=S')
m10 = Chem.MolFromSmiles('Cn1c(CNC(=O)C(=O)Nc2cccc(Cl)c2Cl)nc2ccccc21')
mols = [m1, m2, m3, m4, m5 ,m6, m7, m8, m9, m10]


# generate fingeprints: Morgan fingerprint with radius 2
fps = [AllChem.GetMorganFingerprintAsBitVect(m, 2) for m in mols]

# convert the RDKit explicit vectors into numpy arrays
np_fps = []
for fp in fps:
  arr = np.zeros((1,))
  DataStructs.ConvertToNumpyArray(fp, arr)
  np_fps.append(arr)

# get a random forest regressor with 100 trees
rndf_rgsr = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1, warm_start=False)


#train the random forest
#ys are the caco-2 coefficients we wish to predict
ys_fit = [379.724, 101.644, 3154.167, 97.437, 21.152, 569.981, 150.55, 690.843, 78.866, 984.371]

rndf_rgsr.fit(np_fps, ys_fit)


#use the random forest to predict a new molecule
m_new = Chem.MolFromSmiles('Cc1n[nH]c(Cc2ccc(-n3cnnc3)cc2)n1')      #actual caco2 is 410.037
fp = np.zeros((1,))
DataStructs.ConvertToNumpyArray(AllChem.GetMorganFingerprintAsBitVect(m_new, 2), fp)

print(rndf_rgsr.predict((fp,)))

但是，当我尝试Cc1ccc(NNC(=O)c2ccc(CN3C(=O)CCC3=O)cc2)cc1Cl,379.724使用以下代码处理从文件中导入的大量分子时，该文件包含很多看起来像 的行：

from rdkit import Chem, DataStructs     
from rdkit.Chem import AllChem
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor     #our regressors
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split    
import numpy as np
import pandas as pd
from pandas import DataFrame, read_csv



#import our data from file
df = pd.read_csv('test_db.csv', delimiter=',' )     #a pandas DataFrame


#get the values of variables and targets
X = df["smiles"].values
y = df["Caco2"].values



#split our data set into two parts
x_train, x_eval, y_train, y_eval = train_test_split(X, y, test_size = 0.2, random_state = 42)   


#convert our smiles string into actual molecular graphs
mols_ready_train = [Chem.MolFromSmiles(x_train[i]) for i in range(len(x_train))]
mols_ready_eval = [Chem.MolFromSmiles(x_eval[i]) for i in range(len(x_eval))]

# generate fingeprints: Morgan fingerprint with radius 2    
fing_prints_train = [AllChem.GetMorganFingerprintAsBitVect(m, 2) for m in mols_ready_train]
fing_prints_eval = [AllChem.GetMorganFingerprintAsBitVect(m, 2) for m in mols_ready_eval]


# convert the RDKit explicit vectors into numpy arrays
np_fps_train = []
for fp in fing_prints_train:
  arr = np.zeros((1,))
  DataStructs.ConvertToNumpyArray(fp, arr)
  np_fps_train.append(arr)

np_fps_eval = []
for fp in fing_prints_eval:
  arr = np.zeros((1,))
  DataStructs.ConvertToNumpyArray(fp, arr)
  np_fps_eval.append(arr)  


# get a random forest regressor with 100 trees

rndf_rgsr = RandomForestRegressor(n_estimators=1000, random_state=42, n_jobs=-1, warm_start=False)


#train our random forest regressor
rndf_rgsr.fit(np_fps_train, y_train)



# use the random forest to predict a new molecule
m_new = Chem.MolFromSmiles('Cc1n[nH]c(Cc2ccc(-n3cnnc3)cc2)n1')

fp = numpy.zeros((1,))
DataStructs.ConvertToNumpyArray(AllChem.GetMorganFingerprintAsBitVect(m_new, 2), fp)

print(rndf_rgsr.predict((fp,)))

它崩溃并出现以下错误：

文件“/home/me/predictor.py”，第 55 行，在 rndf_rgsr.fit(np_fps_train, y_train) 文件“/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/forest.py”中，第 248 行，适合 y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None) 文件“/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py” ，第 407 行，在 check_array _assert_all_finite(array) 文件“/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py”中，第 58 行，在 _assert_all_finite 中“或对于 %r 来说太大的值。” % X.dtype) ValueError: 输入包含 NaN、无穷大或对于 dtype('float64') 来说太大的值。

我已经检查过我使用的向量没有包含nans 或infs。这里使用的指纹长度为 2048 位，但我怀疑它们是问题的根源。验证出了点问题，但我真的看不出是什么。你能提供任何提示吗？

ETA：test_db.csv有 50,000 行。我创建了一个tiny_db.csv只有 10 行的模型，在它上面我的模型效果很好（也就是说，它的预测是错误的，但它完全有效）。它也适用于 100 行文件，但对于 1000 行文件则不能，并且会因上述错误而崩溃。进一步的实验表明，250 行有效，但 500 行无效。

ETA：前 250 行有效，但接下来的 250 行（250 到 500）无效。读取超过 100 行时，print(y_train.mean(), y_train.min(), y_train.max())返回(nan,nan,nan). 总而言之，我强烈怀疑问题来自pandas.Dataframe.values，这将我的好系数向上转换为float64，从而导致算术错误，进而导致验证程序崩溃。我认为我最好使用 pythoncsv模块而不是pandas.read_csv与DataFrame.values.

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