VirtuousMultiTaste

Official repo of the Multi Taste Predictor developed in the framework of the EU-funded VIRTUOUS project

The VirtuousMultiTaste is also implemented into a webserver interface at https://virtuous.isi.gr/#/virtuous-multitaste

Repo Structure

The repository is organized in the following folders:

• VirtuousMultiTaste/

Collecting python codes and sources files to run the umami prediction

• data/

Collecting the training and the test sets of the model along with their relative taste predcitions, the external DBs with their relative taste predictions, the food screening results on chocolate and coffee, and the folder with comparison with the state-of-the-art tools

• examples/

Including examples files to test the code

Authors

1. Lampros Androutsos
2. Lorenzo Pallante

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Prerequisites

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1. Create conda environment:

    conda create -n myenv python=3.10
    conda activate myenv
   

2. Install required packages:

    conda install -c conda-forge rdkit chembl_structure_pipeline
    conda install -c mordred-descriptor mordred
    pip install knnimpute joblib Cython scikit-learn==1.1.1 xmltodict pyenchant
   

3. Clone the VirtuousMultiTaste repository from GitHub

    git clone https://github.com/lorenzopallante/VirtuousMultiTaste
   

Enjoy!

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How to use VirtuousMultiTaste

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The main code is VirtuousMultiTaste.py within the VirtuousMultiTaste folder.

To learn how to run, just type:

python VirtuousMultiTaste.py --help

And this will print the help message of the program:

usage: VirtuousMultiTaste.py [-h] [-c COMPOUND] [-f FILE] [-t TYPE] [-d DIRECTORY] [-v]

    VirtuousMultiTaste: ML-based tool to predict the umami taste

    options:
    -h, --help            show this help message and exit
    -c COMPOUND, --compound COMPOUND
                            query compound (allowed file types are SMILES, FASTA, Inchi, PDB, Sequence, Smarts, pubchem name)
    -f FILE, --file FILE  text file containing the query molecules
    -t TYPE, --type TYPE  type of the input file (SMILES, FASTA, Inchi, PDB, Sequence, Smarts, pubchem name). If not specified, an automatic
                            recognition of the input format will be tried
    -d DIRECTORY, --directory DIRECTORY
                            name of the output directory
    -v, --verbose         Set verbose mode

To test the code you can submit an example txt file in the "samples" fodler (test.txt)

The code will create a log file and an output folder containing:

1. "best_descriptors.csv": a csv file collecting the 15 best molecular descriptors for each processed smiles on which the prediction relies
2. "descriptors.csv": a csv file collecting all the calculated molecular descriptors for each processed smiles
3. "result_labels.txt": a txt file containing the predicted taste classes for each processed molecule
4. "result_dominant_labels.txt": a txt file containing the predicted dominant taste classes for each processed molecule
5. "predictions.csv": a csv summarising the results of the prediction

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Acknowledgement

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The present work has been developed as part of the VIRTUOUS project, funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie-RISE Grant Agreement No 872181 (https://www.virtuoush2020.com/).