An overview of molecular hybrids in drug discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The hybridization of biologically active molecules is a powerful tool for drug discovery used to target a variety of diseases. It offers the prospect of better drugs for the treatment of a number of illnesses including cancer, malaria, tuberculosis and AIDS. Hybrid drugs can provide combination therapies in a single multi-functional agent and, by doing so, be more specific and powerful than conventional classic treatments. This research field is in great expansion and attracts many researchers worldwide. AREA COVERED: This review covers the main research published between early 2013 to mid-2015 and takes into account several previous reviews on the subject. Its intention is to showcase the most recent advances reported towards the development of molecular hybrids in drug discovery. Particular attention is given to anticancer hybrids throughout the review. EXPERT OPINION: Current advances show that molecular hybrids of biologically active molecules can lead to powerful therapeutics. Natural products play a key role in this field. It is also believed that toxin hybrids present a great opportunity for future progress and should be further explored. Furthermore, the synthesis of hybrid organometallics should be systematically studied as it can lead to potent drugs. The crucial requirement for growth still remains the efficacy of synthesis. Hence, the development of efficient synthetic methods allowing rapid access to diverse series of hybrids must be further investigated by researchers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it