Sure! On a meta-level, this source code performs the following steps:

    Loading data: The data is loaded from a csv file called "smiles_data.csv" into a pandas data frame df.

    Data cleaning: The data is then cleaned by removing duplicates and tautomers. The protonation states of the molecules are also made appropriate for pH 7.

    Conversion: The SMILES or SMARTS strings are then converted to RDKit molecules and stored in a new column called "Molecules".

    Splitting the data: The data is split into training and testing sets. 80% of the data is used for training, and the remaining 20% is used for testing.

    Preprocessing: The data is then preprocessed by converting the SMILES or SMARTS strings into numerical arrays that can be fed into a machine learning model. This involves tokenizing the text, encoding the sequences, and padding the sequences to the same length.

    Building the model: A small LSTM model is built using the Keras library. The model consists of an embedding layer, a bidirectional LSTM layer, two dense layers, and is compiled with the Adam optimizer and binary crossentropy loss function.

    Training the model: The model is trained using the training data. The accuracy of the model is monitored during the training process.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence