Pnictogens in medicinal chemistry: evolution from erstwhile drugs to emerging layered photonic nanomedicine
Bibliographic record
Abstract
Pnictogens (the non-metal phosphorus, metalloids arsenic and antimony, and metal bismuth) possess diverse chemical characteristics that support the formation of extended molecular structures. As witnessed by the centuries-old (and ongoing) clinical utilities, pnictogen-based compounds have secured their places in history as "magic bullet" therapeutic drugs in medicinal contexts. Moreover, with the development of recent metalloproteomics and bio-coordination chemistry, the pnictogen-based drugs functionally binding to proteins/enzymes in biological systems have been underlaid for "drug repurposing" with promising opportunities. Furthermore, advances in the modern materials science and nonotechnology have stimulated a revolution in other newly discovered forms of pnictogens-phosphorene, arsenene, antimonene, and bismuthine (layered pnictogens). Based on their favorable optoelectronic properties, layered pnictogens have shown dramatic superiority as emerging photonic nanomedicines for the treatment of various diseases. This tutorial review outlines the history and mechanism of action of ancient pnictogen-based drugs (e.g., arsenical compounds in traditional Chinese medicine) and their repurposing into modern therapeutics. Then, the revolutionary use of emerging layered pnictogens as photonic nanomedicines, alongside assessments of their in vivo biosafety, is discussed. Finally, the challenges to further development of pnictogens are set forth and insights for further exploration of their appealing properties are offered. This tutorial review may also provide some deep insights into the fields of integrated traditional Chinese and Western medicines from the perspective of materials science and nanotechnology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".