Recent Advancements in the Diversification and Applications of Boron-Containing Compounds in Medicinal Chemistry
Bibliographic record
Abstract
Boron-containing compounds have made a significant impact on the field of medicinal chemistry since the discovery of Bortezomib (Velcade®), a dipeptide boronic acid approved by the FDA in 2003 for the treatment of multiple myeloma. Since then, over the last two decades, four more boron-containing drugs have been approved by the FDA: Tavaborole (Kerydin®), Ixazomib (Ninlaro®), Crisaborole (Eucrisa®), and Vaborbactam (in Vabomere®). These compounds are approved for treating conditions such as onychomycosis, multiple myeloma, and atopic dermatitis, as well as an Aβ-lactamase inhibitor approved in combination with meropenem for treating infections. Further, many organic molecules containing boron are in clinical trials. Additionally, boron-containing compounds play a crucial role in various biological processes. Boron’s Lewis acidity has been utilized for diverse applications, from targeting biological molecules to the synthesis of organic compounds and in advanced drug delivery systems. Recent progress in the advancement of boron-containing compounds has not stopped, and the further use of Boron is emerging day-by-day with the discovery of multifaceted applications. This review aims to highlight the recent advances made in the last decade in the drug design of boron-containing compounds and their therapeutic applications. Here, in this work, we have focused on the recent diversification and progress of boron-containing compounds in medicinal chemistry applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".