ここでは、SMILESまたはSMARTS文字列をcsvファイルからロードし、予測用の小さなLSTMモデルに入力する前にデータをクリーニングするスクリプトの基本的な実装を示します：

```
import pandas as pd
import numpy as np
import rdkit
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem import Descriptors
from rdkit.Chem import rdmolops
from rdkit.Chem import AllChem

# csvファイルからデータを読み込む
df = pd.read_csv('smiles_data.csv')

# 重複と互変異性体を取り除く
df = df.drop_duplicates()
df = df.apply(rdmolops.RemoveHs, axis=1)

# すべてのプロトン化状態がpH 7に適切であることを確認する
df = df.apply(rdmolops.AssignStereochemistry、axis=1)
df = df.apply(rdmolops.AssignAtomChiralTagsFromStructure, axis=1)
df = df.apply(rdmolops.AssignBondStereochemistry, axis=1)

# SMILESまたはSMARTS文字列をRDKit分子に変換します。
df['Molecules'] = df['SMILES or SMARTS'].apply(Chem.MolFromSmiles)

# データをトレーニングセットとテストセットに分割する
train_df = df.sample(frac=0.8, random_state=0)
test_df = df.drop(train_df.index)

# ラベルを予測する小さなLSTMを作る
import tensorflow as tf
from tensorflow import keras


# 前処理
def create_dataset(df)：
  data = df['SMILES or SMARTS'].to_numpy()
  labels = df['Labels'].to_numpy()
  return data, labels

train_data, train_labels = create_dataset(train_df)
test_data, test_labels = create_dataset(test_df)

# エンコード
tokenizer = keras.preprocessing.text.Tokenizer(num_words=20000)
tokenizer.fit_on_texts(train_data)

train_encoded = tokenizer.texts_to_sequences(train_data)
test_encoded = tokenizer.texts_to_sequences(test_data)

# パディング
train_padded = keras.preprocessing.sequence.pad_sequences(train_encoded, maxlen=100)
test_padded = keras.preprocessing.sequence.pad_sequences(test_encoded, maxlen=100)

# モデルの構築
model = keras.Sequential([)
  keras.layers.Embedding(20000, 64, input_length=100)、
  keras.layers.Bidirectional(keras.layers.LSTM(64))、
  keras.layers.Dense(64, activation='relu')、
  keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

history = model.fit
```
Here is a basic implementation of the script to load the SMILES or SMARTS strings from a csv file and clean the data before feeding it into a small LSTM model for prediction:

import pandas as pd
import numpy as np
import rdkit
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem import Descriptors
from rdkit.Chem import rdmolops
from rdkit.Chem import AllChem

# Load the data from the csv file
df = pd.read_csv('smiles_data.csv')

# Remove duplicates and tautomers
df = df.drop_duplicates()
df = df.apply(rdmolops.RemoveHs, axis=1)

# Ensure all protonation states are appropriate for pH 7
df = df.apply(rdmolops.AssignStereochemistry, axis=1)
df = df.apply(rdmolops.AssignAtomChiralTagsFromStructure, axis=1)
df = df.apply(rdmolops.AssignBondStereochemistry, axis=1)

# Convert the SMILES or SMARTS strings to RDKit molecules
df['Molecules'] = df['SMILES or SMARTS'].apply(Chem.MolFromSmiles)

# Split the data into training and testing sets
train_df = df.sample(frac=0.8, random_state=0)
test_df = df.drop(train_df.index)

# Create a small LSTM to predict the label
import tensorflow as tf
from tensorflow import keras

# Preprocessing
def create_dataset(df):
  data = df['SMILES or SMARTS'].to_numpy()
  labels = df['Labels'].to_numpy()
  return data, labels

train_data, train_labels = create_dataset(train_df)
test_data, test_labels = create_dataset(test_df)

# Encoding
tokenizer = keras.preprocessing.text.Tokenizer(num_words=20000)
tokenizer.fit_on_texts(train_data)

train_encoded = tokenizer.texts_to_sequences(train_data)
test_encoded = tokenizer.texts_to_sequences(test_data)

# Padding
train_padded = keras.preprocessing.sequence.pad_sequences(train_encoded, maxlen=100)
test_padded = keras.preprocessing.sequence.pad_sequences(test_encoded, maxlen=100)

# Building the Model
model = keras.Sequential([
  keras.layers.Embedding(20000, 64, input_length=100),
  keras.layers.Bidirectional(keras.layers.LSTM(64)),
  keras.layers.Dense(64, activation='relu'),
  keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

history = model.fit