Efficient and Rapid Mechanochemical Assembly of Platinum(II) Squares for Guanine Quadruplex Targeting
Bibliographic record
Abstract
We present a rapid and efficient method to generate a family of platinum supramolecular square complexes, including previously inaccessible targets, through the use of ball milling mechanochemistry. This one-pot, two-step process occurs in minutes and enables the synthesis of the squares [Pt 4 (en) 4 (N∩N) 4 ][CF 3 SO 3 ] 8 (en= ethylenediamine, N∩N = 4,4′-bipyridine derivatives) from commercially available precursor K 2 PtCl 4 in good to excellent yields. In contrast, solution-based assembly requires heating the reagents for weeks and gives lower yields. Mechanistic investigations into this remarkable rate acceleration revealed that solution-based assembly (refluxing for days) results in the formation of large oligomeric side-products that are difficult to break down into the desired squares. On the other hand, ball milling in the solid state is rapid and appears to involve smaller intermediates. We examined the binding of the new supramolecular squares to guanine quadruplexes, including oncogene and telomere-associated DNA and RNA sequences. Sub-micromolar binding affinities were obtained by fluorescence displacement assays (FID) and isothermal titration calorimetry (ITC), with binding preference to telomere RNA (TERRA) sequences. ITC showed a 1:1 binding stoichiometry of the metallosquare to TERRA, while the stoichiometry was more complex for telomeric quadruplex DNA and a double-stranded DNA control.
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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) | 0.001 | 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 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".