BitterTranslate: A Natural Language Processing and Machine Learning-Based Framework for Mapping Bitter Taste Receptor Agonism
Bibliographic record
Abstract
Bitter taste receptors (TAS2Rs) are G protein-coupled receptors (GPCRs) expressed most notably on the tongue, but also in many extraoral tissues. TAS2R ligands are used for improving bitter-drug compliance, treating various illnesses, and studying the receptors' extraoral functions. Although machine learning, a promising drug discovery tool, can be used to predict TAS2R activators, obtaining high-quality features for training is time-intensive and reliant on specialized software. This work explores the potential of transformers (a neural network architecture for extracting features from sequential text strings that has revolutionized natural language processing-based tasks) for predicting these ligands. Hence, BitterTranslate, a TAS2R-agonist prediction algorithm, needs only the Simplified Molecular-Input Line-Entry System (SMILES) string of the ligand and the amino acid sequence of the TAS2R. The algorithm was built using two Bidirectional Encoder Representations from Transformers (BERT) models: one trained on small molecules to extract ligand features and the other trained on GPCRs to extract receptor features. An XGBoost classifier was pretrained on a large GPCR-ligand data set and fine-tuned on the smaller TAS2R-ligand data set. BitterTranslate predicts ligand associations with 80% precision and 65% recall across all TAS2Rs and 83% precision and 88% recall for the receptor with the most data: TAS2R14.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".