ArticleFrontiers in chemistry2018
BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds.
Article in Frontiers in chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Bitter Compounds in Medicinal Food Plants Based on Traditional Chinese Medicine: Analysis and Regulation Strategies from Chemical Structure to Perception Mechanisms.Molecules (Basel, Switzerland) · 2026Review
- Elucidating the Material Basis and Receptor Mechanism of Bitterness inFoods (Basel, Switzerland) · 2026Article
- Bitter Taste Signalling via TAS2R43 Enhances Temozolomide Efficacy in Glioblastoma Cells.International journal of molecular sciences · 2026Article
- Article
- AI-driven prediction of bitterness and sweetness and analysis of receptor interactions.Current research in food science · 2025Article
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- Investigating the effects of thermal processing on bitter substances in atemoya (Food chemistry: X · 2024Article
- FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data.Journal of cheminformatics · 2024Article
- Predicting multiple taste sensations with a multiobjective machine learning method.NPJ science of food · 2024Article
- Computational gastronomy: capturing culinary creativity by making food computable.NPJ systems biology and applications · 2024Review
- Article
- Prediction of bitterness based on modular designed graph neural network.Bioinformatics advances · 2024Article
- A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction.Foods (Basel, Switzerland) · 2023Article
- Data-Driven Elucidation of Flavor Chemistry.Journal of agricultural and food chemistry · 2023Review
- SuperNatural 3.0-a database of natural products and natural product-based derivatives.Nucleic acids research · 2023Article
- Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.Scientific reports · 2022Article
- Machine learning based attribution mapping of climate related discussions on social media.Scientific reports · 2022Article
- The Taste-Masking Mechanism of Chitosan at the Molecular Level on Bitter Drugs of Alkaloids and Flavonoid Glycosides from Traditional Chinese Medicine.Molecules (Basel, Switzerland) · 2022Article
- Article
- Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with deep graph networks.PLoS computational biology · 2022Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Taste of a chemical compound 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 score 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 using the feature space of a circular fingerprint.
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