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Informed classification of sweeteners/bitterants compounds via explainable machine learning.

Gabriele Maroni1, Lorenzo Pallante2, Giacomo Di Benedetto3

  • 1Dalle Molle Institute for Artificial Intelligence IDSIA - USI/SUPSI, Via La Santa 1, CH-6962, Lugano-Viganello, Switzerland.

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Summary

This study introduces machine learning and SHapley Additive exPlanations (SHAP) to classify sweet and bitter tastes. It identifies key molecular features for designing better sweeteners and bitterants.

Keywords:
BitterantsExplainable machine learningNatural compoundsSweet/bitter dichotomySweetener

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Area of Science:

  • Computational chemistry and cheminformatics
  • Sensory science and gustation
  • Machine learning applications in molecular discovery

Background:

  • Taste perception involves complex molecular interactions with taste receptors.
  • Sweet and bitter taste classification is a significant area for machine learning research.
  • Existing classifiers can be improved by deeper understanding of molecular taste determinants.

Purpose of the Study:

  • To develop and evaluate machine learning strategies for classifying sweet and bitter tastes.
  • To utilize SHapley Additive exPlanations (SHAP) for identifying key molecular descriptors.
  • To facilitate rational design and screening of sweeteners and bitterants.

Main Methods:

  • Development and testing of multiple machine learning models for taste classification.
  • Application of SHapley Additive exPlanations (SHAP) to interpret model predictions.
  • Identification of significant chemical descriptors influencing sweetness and bitterness.

Main Results:

  • Successful classification of sweet and bitter taste compounds using machine learning.
  • SHAP analysis revealed critical molecular features driving taste perception.
  • The study provides a foundation for informed molecular design of taste modifiers.

Conclusions:

  • Machine learning coupled with SHAP offers a powerful approach for understanding taste classification.
  • This research aids in the rational design and screening of novel sweeteners and bitterants.
  • Publicly available datasets and models are provided to advance future research in taste prediction.