Evaluating Metal–Ligand Interactions of Metal-Binding Isosteres Using Model Complexes
Bibliographic record
Abstract
Bioisosteres are a useful approach to address pharmacokinetic liabilities and improve drug-like properties. Specific to developing metalloenzyme inhibitors, metal-binding pharmacophores (MBPs) have been combined with bioisosteres, to produce metal-binding isosteres (MBIs) as alternative scaffolds for use in fragment-based drug discovery (FBDD). Picolinic acid MBIs have been reported and evaluated for their metal-binding ability, pharmacokinetic properties, and enzyme inhibitory activity. However, their structural, electronic, and spectroscopic properties with metal ions other than Zn(II) have not been reported, which might reveal similarities and differences between MBIs and the parent MBPs. To this end, [M(TPA)(MBI)]+ (M = Ni(II) and Co(II), TPA = tris(2-pyridylmethyl)amine) is presented as a bioinorganic model system for investigating picolinic acid, four heterocyclic MBIs, and 2,2′-bipyridine. These complexes were characterized by X-ray crystallography as well as NMR, IR, and UV–vis spectroscopies, and their magnetic moments were accessed. In addition, [(TpPh,Me)Co(MBI)] (TpPh,Me = hydrotris(3,5-phenylmethylpyrazolyl)borate) was used as a second model compound, and the limitations and attributes of the two model systems are discussed. These results demonstrate that bioinorganic model complexes are versatile tools for metalloenzyme inhibitor design and can provide insights into the broader use of MBIs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".