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.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".