Phosphonate Chelators for Medicinal Metal Ions
Bibliographic record
Abstract
A family of phosphonate-bearing chelators was synthesized to study their potential in metal-based (radio)pharmaceuticals. Three ligands (H 6 phospa, H 6 dipedpa, H 6 eppy; structures illustrated in manuscript) were fully characterized, including X-ray crystallographic structures of H 6 phospa and H 6 dipedpa. NMR spectroscopy techniques were used to confirm the complexation of each ligand with selected trivalent metal ions. These methods were particularly useful in discerning structural information for Sc 3+ and La 3+ complexes. Solution studies were conducted to evaluate the complex stability of 15 metal complexes. As a general trend, H 6 phospa was noted to form the most stable complexes, and H 6 eppy associated with the least stable complexes. Moreover, In 3+ complexes were determined to be the most stable, and complexes with La 3+ were the least stable, across all metals. Density functional theory (DFT) was employed to calculate structures of H 6 phospa and H 6 dipedpa complexes with La 3+ and Sc 3+ . A comparison of experimental 1 H NMR spectra with calculated 1 H NMR spectra using DFT-optimized structures was used as a method of structure validation. It was noted that theoretical NMR spectra were very sensitive to a number of variables, such as ligand configuration, protonation state, and the number/orientation of explicit water molecules. In general, the inclusion of an explicit second shell of water molecules qualitatively improved the agreement between theoretical and experimental NMR spectra versus a polarizable continuum solvent model alone. Formation constants were also calculated from DFT results using potential-energy optimized structures. Strong dependence of molecular free energies on explicit water molecule number, water molecule configuration, and protonation state was observed, highlighting the need for dynamic data in accurate first-principles calculations of metal–ligand stability constants.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".