ArticleScientific reports2019
BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 74 citations in OpenAlex.
- FlavorGPN: a graph neural network for multi-label molecular flavor prediction.Molecular diversity · 2026Article
- Machine learning for food flavor prediction and regulation: models, data integration, and future perspectives.Journal of advanced research · 2026Review
- 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
- Molecular and Computational Basis of Taste Perception: A Review toward the "Digital Language of Taste".ACS omega · 2026Review
- Elucidating the Material Basis and Receptor Mechanism of Bitterness inFoods (Basel, Switzerland) · 2026Article
- Electrochemical Sensors as a Tool for Taste Perception in Pharmaceutical Products: Advances and Perspectives.Biosensors · 2026Review
- Discovery of Novel Minor Steviol Glycoside from theBiomolecules · 2026Article
- Computational screening of umami tastants using deep learning.Molecular diversity · 2025Article
- A chemical language model for molecular taste prediction.NPJ science of food · 2025Article
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- AI-driven prediction of bitterness and sweetness and analysis of receptor interactions.Current research in food science · 2025Article
- 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
- Predicting and improving complex beer flavor through machine learning.Nature communications · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- 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
- Machine learning models to predict micronutrient profile in food after processing.Current research in food science · 2023Article
- Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dichotomy of sweet and bitter tastes is a salient evolutionary feature of human gustatory system with an innate attraction to sweet taste and aversion to bitterness. A better understanding of molecular correlates of bitter-sweet taste gradient is crucial for identification of natural as well as synthetic compounds of desirable taste on this axis. While previous studies have advanced our understanding of the molecular basis of bitter-sweet taste and contributed models for their identification, there is ample scope to enhance these models by meticulous compilation of bitter-sweet molecules and utilization of a wide spectrum of molecular descriptors. Towards these goals, our study provides a structured compilation of bitter, sweet and tasteless molecules and state-of-the-art machine learning models for bitter-sweet taste prediction (BitterSweet). We compare different sets of molecular descriptors for their predictive performance and further identify important features as well as feature blocks. The utility of BitterSweet models is demonstrated by taste prediction on large specialized chemical sets such as FlavorDB, FooDB, SuperSweet, Super Natural II, DSSTox, and DrugBank. To facilitate future research in this direction, we make all datasets and BitterSweet models publicly available, and present an end-to-end software for bitter-sweet taste prediction based on freely available chemical descriptors.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.