Write a python script to load a list of SMILES or SMARTS strings from a csv file. Clean the input data and remove any duplicates or tautomers. Make sure all protonation states are appropriate for pH 7. Create a small LSTM to predict the label found in the csv file.
Here is a basic outline of the steps required:

Read the CSV file and extract the SMILES/SMARTS strings and labels
Clean the SMILES/SMARTS strings using RDKit - remove duplicates, standardize tautomers/protomers, etc.
Create LSTM model
Train the model on the cleaned data
Evaluate the model's performance

So in code it would look something like:
import pandas as pd
from rdkit import Chem
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import LSTM, Dense

# Read CSV and extract data
df = pd.read_csv('data.csv')
smiles = df['SMILES'].tolist()
labels = df['Label'].tolist()

# Clean SMILES
cleaned_smiles = [Chem.MolToSmiles(Chem.MolFromSmiles(s)) for s in smiles]

# Remove duplicates
cleaned_smiles = list(set(cleaned_smiles))

# train/test split and create/train model
X_train, X_test, y_train, y_test = train_test_split(cleaned_smiles, labels)
model = Sequential([LSTM(32), Dense(1, activation='sigmoid')])
model.fit(X_train, y_train)

# Evaluate
model.evaluate(X_test, y_test)

This is just a brief outline, many additional details would need to be added for a full working script. Let me know if you would like help with any specific part of the code.