Khanna, V., Roni, R. A., Bouamria, S., Yu, N., Liu, C., Zuniga, C., & Zhao, J. (2026). BitterNova: A multi-source machine learning framework for accurate and interpretable prediction of molecular bitterness.
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BitterNova: Molecular Bitterness Prediction
A collaborative, interpretable machine-learning framework for predicting molecular bitterness from multi-source scientific data.
- Question
- Can molecular bitterness be predicted accurately and interpretably from multiple scientific data sources using machine learning?
- Why it matters
- Bitterness is a decisive sensory attribute in foods and ingredients. Interpretable prediction from molecular information can support screening and formulation.
- My role
- Co-author and research contributor (second author).
- Status
- Manuscript in preparation.
- Methods
- Multi-source scientific datasets
- Machine learning with molecular descriptors
- Interpretation of model predictions
- Outputs
- BitterNova manuscript in preparation (co-author)
Overview
BitterNova is a collaborative machine-learning project on molecular bitterness prediction in Dr. Jing Zhao's research group, combining multi-source scientific data, molecular descriptors, and an emphasis on interpretable predictions.