Progress in the Rational Design for Polypharmacology Drug
Bibliographic record
Abstract
BACKGROUND: Polypharmacology plays an important role in drug discovery. Polypharmacology drugs strategy provides a novel way in drug design. However, to develop a polypharmacology drug with desired profile remains a challenge. METHODS: Owing to the huge progress in computational biology and chemistry, the rational drug design is becoming increasingly important in discovery of polypharmacology drug. RESULTS: Several methodologies on the rational polypharmacology drug design have been developed, which are summarized and classified as ligand based design in polypharmacology, target based design in polypharmacology, and the hybrid of ligand and target based design in polypharmacology. CONCLUSION: We give an overview of the importance of polypharmacology in drug design and current trends in rational design of polypharmacology, which may be beneficial to the design and development of polypharmacology drugs.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".