🦠 Model Request: Predict bioactivity against Main Protease of SARS-CoV-2

[HarmonySosa]

Model Name

Predict bioactivity against Main Protease of SARS-CoV-2

Model Description

MProPred predicts the efficacy of compounds against the main protease of SARS-CoV-2, which is a promising drug target since it processes polyproteins of SARS-CoV-2. This model uses PaDEL-Descriptor to calculate molecular descriptors of compounds. It is based on a dataset of 758 compounds that have inhibition efficacy against the Main Protease, as published in peer-reviewed journals between January, 2020 and August, 2021. Input compounds are compared to compounds in the dataset to measure molecular similarity with MACCS.

Slug

mpro-covid19

Tag

COVID19

Publication

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289339/

Source Code

https://github.com/Nadimfrds/Mpropred

License

MIT

[GemmaTuron]

Hi @HarmonySosa

Good start, a couple comments before we approve the request: I think the tags will not work as they are not from the approved list (in GitBook) - they are python-based so strings need to match Slugs also have a word limit, something like mpro-covid19 would be better

Can you modify those fields before we approve the model?

[GemmaTuron]

/approve

[github-actions[bot]]

New Model Repository Created! 🎉

@HarmonySosa ersilia model respository has been successfully created and is available at:

🔗 ersilia-os/eos3nn9

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[GemmaTuron]

Hi @HarmonySosa !

This model is using PADEL Descriptors to calculate MACCS Fingerprints. In our experience, the PADEL package is not very well integrated with Python and can bring problems. Can you try to see if the MACCS fingerprints we obtain with RDKIT (MACCS Keys) are the same as the ones we obtain with the MPro Predictor.

It should be something like.. (making the function up, look for the right one)

from rdkit.Chem import MACCSKeys
maccskeys = [MACCSKeys(smi) for smi in smiles_list]

This will allow us to modify the calculate descriptors function:

# Molecular descriptor calculator option
    def desc_calc():
        # Performs the descriptor calculation
        bashCommand = "java -Xms2G -Xmx2G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.jar -removesalt -standardizenitro -fingerprints -descriptortypes ./PaDEL-Descriptor/MACCSFingerprinter.xml -dir ./ -file descriptors_output.csv"
        process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
        output, error = process.communicate()
        os.remove('molecule.smi')

[HarmonySosa]

Hi @GemmaTuron!

Here are the desc_calc and build_model functions using PADEL:

def desc_calc():
    # Performs the descriptor calculation
    bashCommand = "java -Xms2G -Xmx2G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.jar -removesalt -standardizenitro -fingerprints -descriptortypes ./PaDEL-Descriptor/MACCSFingerprinter.xml -dir ./ -file descriptors_output.csv"
    process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
    output, error = process.communicate()
    os.remove('molecule.smi')

def build_model(input_data):
    # Reads in saved regression model
    load_model = pickle.load(open('Mpro_model.pkl', 'rb')) #  06.25.24 broken up to handle dtype error
    # Apply model to make predictions
    prediction = load_model.predict(input_data)
    st.header('**Prediction results**')
    prediction_output = pd.Series(prediction, name='pIC50')
    molecule_name = pd.Series(load_data[1], name='molecule_name')
    df = pd.concat([molecule_name, prediction_output], axis=1)
    st.write(df)
    st.markdown(filedownload(df), unsafe_allow_html=True)

These are the results I get when I run the model with PADEL:

This is how I have been trying to use RDKit, but I get different results:

Convert RDKit bit vector to a list of ints

def bitvector_to_list(bitvector):
    return [int(bit) for bit in bitvector]

def calculate_maccs_keys(smiles_list):
    maccs_keys = []
    for smi in smiles_list:
        mol = Chem.MolFromSmiles(smi)
        if mol is not None:
            maccs_key = MACCSkeys.GenMACCSKeys(mol)
            maccs_keys.append(bitvector_to_list(maccs_key))
        else:
            maccs_keys.append([0]*167)  # MACCS keys are 167 bits long
    return maccs_keys

def desc_calc(smiles_list, output_file='descriptors_output.csv'):
    # Calculate MACCS fingerprints using RDKit
    maccs_keys = calculate_maccs_keys(smiles_list)

     # Create a DataFrame and name columns appropriately, save to CSV
    df = pd.DataFrame(maccs_keys, columns=[f'MACCSFP{i}' for i in range(167)]) 
    df.to_csv(output_file, index=False)
    return df

# Model building section
def build_model(input_data):
    # Reads in saved regression model
    load_model = pickle.load(open('Mpro_model.pkl', 'rb')) #  06.25.24 dtype error
    # Apply model to make predictions
    prediction = load_model.predict(input_data)
    st.header('**Prediction results**')
    prediction_output = pd.Series(prediction, name='pIC50')
    molecule_name = pd.Series(load_data[1], name='molecule_name')
    df = pd.concat([molecule_name, prediction_output], axis=1)
    st.write(df)
    st.markdown(filedownload(df), unsafe_allow_html=True)

These are the results I get when I use RDKit:

[GemmaTuron]

Hi @HarmonySosa I am trying to reporduce the results but just with the molecule_name I cannot get the smiles. Where did you get the molecules from?

[GemmaTuron]

mm in any case it does seem the rdkit implementation and the PADEL descriptors differ slightly, quite surprising as the MACCS Keys are just substructure searchers in a way. In any case, it must be due to the preprocessing that PADEL does vs the preprocessing that rdkit does. We can go ahead and use PADEL in the model, guess by just keeping the folder there it should work