The VirtuousUmami tool predict the umami/non-umami taste of query molecules based on their molecular structures.

Pallante, L., Korfiati, A., Androutsos, L., Stojceski, F., Bompotas, A., Giannikos, I., Raftopoulos, C., Malavolta, M., Grasso, G., Mavroudi, S., Kalogeras, A., Martos, V., Amoroso, D., Piga, D., Theofilatos, K., & Deriu, M. A. (2022). Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Scientific Reports, 12(1), 21735. https://doi.org/10.1038/s41598-022-25935-3

The VirtuousUmami is also implemented into a webserver interface at http://195.251.58.251:19009/#/virtuous-umami

This tool was developed within the Virtuous Project (https://virtuoush2020.com/)

The repository is organized in the following folders:

• VirtuousUmami/

Collecting python codes and sources files to run the umami prediction

• data/

Collecting the training and the test sets of the model, the prioritized list of molecular descriptors and the external DBs with their relative umami predictions

• samples/

Including examples files to test the code

1. Lorenzo Pallante
2. Aigli Korfiati
3. Lampros Androutsos

1. Create conda environment:

    conda create -n myenv python=3.8
    conda activate myenv
   

2. Install required packages:

    conda install -c conda-forge rdkit chembl_structure_pipeline
    conda install -c mordred-descriptor mordred
    pip install tqdm knnimpute joblib Cython scikit-learn==0.22.2 xmltodict
   

3. Clone the VirtuousUmami repository from GitHub

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

Enjoy!

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

To learn how to run, just type:

python VirtuousUmami.py --help

And this will print the help message of the program:

usage: VirtuousUmami.py [-h] [-c COMPOUND] [-f FILE] [-d DIRECTORY] [-v]

VirtuousUmami: ML-based tool to predict the umami taste

optional arguments:
  -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

  -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 12 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": a txt file containing the predicted taste classes (umami/non-umami) for each processed smiles
4. "predictions.csv": a csv summarising the results of the prediction

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/).