```
import csv
import re
import rdkit
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem import AllChem
from rdkit.Chem import rdmolfiles
from collections import defaultdict
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

# Load the csv file into a pandas dataframe
df = pd.read_csv('file.csv')

# Remove duplicates and tautomers
smiles_list = list(set(df['smiles']))
processed_smiles = []
for smile in smiles_list:
    mol = Chem.MolFromSmiles(smile)
    if mol is None:
        continue
    Chem.SanitizeMol(mol)
    smile = Chem.MolToSmiles(mol, isomericSmiles=False)
    processed_smiles.append(smile)

# Clean the input data and make sure all protonation states are appropriate for pH 7
neutral_smiles = []
for smile in processed_smiles:
    mol = Chem.MolFromSmiles(smile)
    charged_mol = Chem.AddHs(mol)
    AllChem.NeutralizeCharges(charged_mol)
    neutral_smile = Chem.MolToSmiles(charged_mol)
    neutral_smiles.append(neutral_smile)

# Create a dictionary mapping the SMILES to their corresponding labels
smiles_dict = defaultdict(int)
for i, row in df.iterrows():
    smiles = row['smiles']
    label = row['label']
    smiles_dict[smiles] = label

# Use the dictionary to create a list of tuples containing the SMILES and labels
smiles_labels = []
for smile in neutral_smiles:
    label = smiles_dict[smile]
    smiles_labels.append((smile, label))

# Split the data into training and testing sets
train_smiles, test_smiles, train_labels, test_labels = train_test_split(neutral_smiles, smiles_labels, test_size=0.2, random_state=42)

# Convert the SMILES strings into numerical arrays
def smiles_to_array(smiles):
    mol = Chem.MolFromSmiles(smile)
    array = np.zeros((1,))
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2)
    for i in fp.GetOnBits():
        array[i] = 1
    return array

train_arrays = [smiles_to_array(smile) for smile in train_smiles]
test_arrays = [smiles_to_array(smile) for smile in test_smiles]

# Create a small LSTM to predict the label
model = Sequential()
model.add(LSTM(100, input_shape=(1,)))
model.add(Dense(100, activation='elu')
model.add(Dense('label size', activation='softmax)
model.compile(' your options here")
model.fit(train_array)
```
import csv
import re
import rdkit
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem import AllChem
from rdkit.Chem import rdmolfiles
from collections import defaultdict
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

# Load the csv file into a pandas dataframe
df = pd.read_csv('file.csv')

# Remove duplicates and tautomers
smiles_list = list(set(df['smiles']))
processed_smiles = []
for smile in smiles_list:
    mol = Chem.MolFromSmiles(smile)
    if mol is None:
        continue
    Chem.SanitizeMol(mol)
    smile = Chem.MolToSmiles(mol, isomericSmiles=False)
    processed_smiles.append(smile)

# Clean the input data and make sure all protonation states are appropriate for pH 7
neutral_smiles = []
for smile in processed_smiles:
    mol = Chem.MolFromSmiles(smile)
    charged_mol = Chem.AddHs(mol)
    AllChem.NeutralizeCharges(charged_mol)
    neutral_smile = Chem.MolToSmiles(charged_mol)
    neutral_smiles.append(neutral_smile)

# Create a dictionary mapping the SMILES to their corresponding labels
smiles_dict = defaultdict(int)
for i, row in df.iterrows():
    smiles = row['smiles']
    label = row['label']
    smiles_dict[smiles] = label

# Use the dictionary to create a list of tuples containing the SMILES and labels
smiles_labels = []
for smile in neutral_smiles:
    label = smiles_dict[smile]
    smiles_labels.append((smile, label))

# Split the data into training and testing sets
train_smiles, test_smiles, train_labels, test_labels = train_test_split(neutral_smiles, smiles_labels, test_size=0.2, random_state=42)

# Convert the SMILES strings into numerical arrays
def smiles_to_array(smiles):
    mol = Chem.MolFromSmiles(smile)
    array = np.zeros((1,))
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2)
    for i in fp.GetOnBits():
        array[i] = 1
    return array

train_arrays = [smiles_to_array(smile) for smile in train_smiles]
test_arrays = [smiles_to_array(smile) for smile in test_smiles]

# Create a small LSTM to predict the label
model = Sequential()
model.add(LSTM(100, input_shape=(1,)))
model.add(Dense(100, activation='elu')
model.add(Dense('label size', activation='softmax)
model.compile(' your options here")
model.fit(train_array)