Multifunctional Ligands in Medicinal Inorganic Chemistry- Current Trends and Future Directions
Bibliographic record
Abstract
This review will highlight recent advances in ligand design for innovative applications in medicinal inorganic chemistry. Ligands that effectively bind metal ions and also include specific features to enhance targeting, reporting, and overall efficacy are driving innovation in areas of disease diagnosis and therapy. Increasing the potency of therapeutic compounds, while limiting side-effects, is a common goal in medicinal chemistry. In an effort to achieve this goal, compounds are being developed that either target a disease site, or are activated by a disease specific biological process. Metal complexes containing targeting functions and/or bioactive ligands, as well as agents that are activated by specific enzymes, or changes in pH and pO2, provide new avenues for drug development. Radiodiagnostic compounds, magnetic resonance imaging agents, and optical probes containing transition metals offer versatility unavailable to organic imaging agents. In certain cases, dual modality agents have been developed, and will be highlighted. Finally, we will discuss targeted metal binding compounds for treatment of metal overload disorders, and the recent application to neurodegenerative disease. Keywords: Medicinal inorganic chemistry, ligand design, multifunctional, diagnostic, therapeutic, dual modality imaging, metal sequestration, specific biological process, Radiodiagnostic compounds, targeted metal binding compounds, enzymatic transformations, metal ion, multifunctional ligand design, antiparasitic agents
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".