PbsNRs: predict the potential binders and scaffolds for nuclear receptors
Bibliographic record
Abstract
Nuclear receptors (NRs) are a class of essential proteins that regulate the expression of specific genes and are associated with multiple diseases. In silico methods for prescreening potential NR binders with predictive binding ability are highly desired for NR-related drug development but are rarely reported. Here, we present the PbsNRs (Predicting binders and scaffolds for Nuclear Receptors), a user-friendly web server designed to predict the potential NR binders and scaffolds through proteochemometric modeling. The utility of PbsNRs was systemically evaluated using both chemical compounds and natural products. Results indicated that PbsNRs achieved a good prediction performance for chemical compounds on internal (ROC-AUC = 0.906, where ROC is Receiver-Operating Characteristic curve and AUC is the Area Under the Curve) and external (ROC-AUC = 0.783) datasets, outperforming both compound-ligand interaction tools and NR-specific predictors. PbsNRs also successfully identified bioactive chemical scaffolds for NRs by screening massive natural products. Moreover, the predicted bioactive and inactive natural products for NR2B1 were experimentally validated using biosensors. PbsNRs not only aids in screening potential therapeutic NR binders but also helps discover the essential molecular scaffold and guide the drug discovery for multiple NR-related diseases. The PbsNRs web server is available at http://pbsnrs.badd-cao.net.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".