BitterDB: 2024 update on bitter ligands and taste receptors
Bibliographic record
Abstract
BitterDB (http://bitterdb.agri.huji.ac.il) was introduced in 2012 as a central resource for information on bitter-tasting molecules and their receptors, and was updated in 2019. The information in BitterDB is used for tasks such as exploring the bitter chemical space, choosing suitable ligands for experimental studies, analyzing receptors' selectivity and promiscuity, and developing machine learning predictors for taste. Here, we describe a major upgrade of the database, including significant increase in content as well as new features. BitterDB now holds over 2200 bitter molecules. For ∼700 molecules, at least one associated bitter taste receptor (TAS2R) is reported. The overall number of ligand-TAS2R associations is now close to 1800. BitterDB is extended to a total of 66 species (including dog, birds, fishes and primates). Following advances in computational structure prediction by AlphaFold and related methods, and the experimental determination of TAS2R structures by cryo-electron microscopy, BitterDB provides links to available structures of TAS2Rs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".