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Front. Chem. | doi: 10.3389/fchem.2018.00093

BitterSweetForest: A random forest based binary classifier to predict bitterness and sweetness of chemical compounds

 Priyanka Banerjee1* and Robert Preissner1*
  • 1Charité Universitätsmedizin Berlin, Germany

Taste of a chemical compounds present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96 % and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10 % of the natural product space as sweet with confidence score of 0.60 and above. 77 % of the approved drug set was predicted as bitter and 2% as sweet with a confidence scores of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds from the feature space of a circular fingerprint.

Keywords: random forest, bitter prediction, sweetness prediction, fingerprints, KNIME workflow, Taste prediction

Received: 01 Dec 2017; Accepted: 14 Mar 2018.

Edited by:

Ramon Carbó-Dorca, University of Girona, Spain

Reviewed by:

Yannick Carissan, Aix-Marseille Université, France
Andrey A. Toropov, Dipartimento di Scienze della Salute Ambientale, Istituto Di Ricerche Farmacologiche Mario Negri (IRCCS), Italy  

Copyright: © 2018 Banerjee and Preissner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Priyanka Banerjee, Charité Universitätsmedizin Berlin, Berlin, Germany,
Dr. Robert Preissner, Charité Universitätsmedizin Berlin, Berlin, Germany,