Molecular hybridization: a powerful tool for multitarget drug discovery
Bibliographic record
Abstract
INTRODUCTION: The current drug discovery paradigm of 'one drug, multiple targets' has gained attention from both the academic medicinal chemistry community and the pharmaceutical industry. This is in response to the urgent need for effective agents to treat multifactorial chronic diseases. The molecular hybridization strategy is a useful tool that has been widely explored, particularly in the last two decades, for the design of multi-target drugs. AREAS COVERED: This review examines the current state of molecular hybridization in guiding the discovery of multitarget small molecules. The article discusses the design strategies and target selection for a multitarget polypharmacology approach to treat various diseases, including cancer, Alzheimer's disease, cardiac arrhythmia, endometriosis, and inflammatory diseases. EXPERT OPINION: Although the examples discussed highlight the importance of molecular hybridization for the discovery of multitarget bioactive compounds, it is notorious that the literature has focused on specific classes of targets. This may be due to a deep understanding of the pharmacophore features required for target binding, making targets such as histone deacetylases and cholinesterases frequent starting points. However, it is important to encourage the scientific community to explore diverse combinations of targets using the molecular hybridization strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".