Advances in the development of hybrid anticancer drugs
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: Hybrid anticancer drugs are of great therapeutic interests as they can potentially overcome most of the pharmacokinetic drawbacks encountered when using conventional anticancer drugs. In fact, the future of hybrid anticancer drugs is very bright for the discovery of highly potent and selective molecules that triggers two or more cytocidal pharmacological mechanisms of action acting in synergy to inhibit cancer tumor growth. AREAS COVERED: This review represents the most advanced and recent data in the field of hybrid anticancer agents covering mainly the past 5 years of research. It also accounts for other significant reviews already published on the topic of anticancer hybrids. The review showcases the research that is at the leading edge of hybrid anticancer drug discovery. The main areas covered by the present review are: DNA alkylating agent hybrids (e.g., platinum(II), nitrogen mustard, etc.), vitamin-D receptor, agonist-histone deacetylase inhibitors, combi-molecule therapies and other types of hybrid anticancer agents. EXPERT OPINION: The current development in the field describes strategies that have never been used before for the design of hybrid anticancer drugs. The information currently available and described in this section allows us to identify the main parameters required to design such molecules. It also provides a clear view of the future directions that must be explored for the successful development and discovery of useful hybrid anticancer drugs.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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