Machine-learning driven structural space exploration for the characterization of the sweetness and bitterness profile of the most extensive set of organic compounds

2025/12/04 by Shilpayan Ghosh, Sapna Kumari Pandey, Kunal Roy · 1 voice
Computer Science · Engineering · Nursing · #Advanced Chemical Sensor Technologies #Biochemical Analysis and Sensing Techniques #Computational Drug Discovery Methods

paper · doi:10.1016/j.jics.2025.102322

openalex publication_date 2025/12/04 · crossref created 2025/12/04 · openalex created_date 2025/12/04 · crossref issued 2026/01/01 · crossref published 2026/01/01 · crossref published-print 2026/01/01 · crossref deposited 2026/03/18 · crossref indexed 2026/03/19 · openalex updated_date 2026/06/15

Citations

• Logistic Regression: A Brief Primer
• Correlation and variable importance in random forests
• Why do we like sweet taste: A bitter tale?
• Classification Model Evaluation Metrics
• ChemTastesPredictor: An ensemble of machine learning classifiers to predict the taste of molecular tastants
• Development of Machine Learning‐Based Models for Mutagenicity Predictions with Applications to Non‐Sugar Sweeteners

Discussions

• 📘 New paper (Ghosh et al., 2026): interpretable ML/QSPR models to predict sweet vs non-sweet and bitter vs non-bitter compounds on a large curated dataset. Paper: doi.org/10.1016/j.ji... #QSPR #bitt [bsky, 1 points, 1 comments]