Theoretical analysis of <i>trans</i>-[PtCl<sub>2</sub>(NH<sub>3</sub>)(thiazole)] and <i>trans</i>-[PtCl<sub>2</sub>(thiazole)<sub>2</sub>] binding to biological targets — Factors influence binding kinetics and adduct stability
Bibliographic record
Abstract
Full reaction energy profiles for trans-[PtCl2(NH3)(thiazole)] and trans-[PtCl2(thiazole)2] binding to sulfur- and nitrogen-containing biorelevant ligands were constructed by the density functional theory (DFT) method. Calculated results demonstrate that trans-platinum complexes can interact with biological targets, affording cis and trans products via very similar transition states. For different substituents, sulfur-containing ligands constitute kinetically preferred targets for platination, whereas the platination of nitrogen-containing ligands is more favorable thermodynamically. This is consistent with previous experimental studies. Calculated results also suggest that the trans effect, the influence of the ligand, the size of the ligand, and hydrogen bonding play important roles in binding kinetics and stabilizing adducts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".