The Interaction of Metal Compounds with Protein Targets: New Tools in Medicinal Chemistry and Chemical Biology
Bibliographic record
Abstract
Abstract In cells and organisms, metal complexes can be specifically designed to interact with biomolecules and accordingly alter important biological processes. These interactions have been widely explored for targeting specific biological functions and diseases. In fact, several studies have demonstrated that inorganic chemistry offers significant diversity and versatility for the preparation of highly potent protein modulators (e.g., inhibitors). Moreover, both coordination and organometallic complexes featuring favorable chemico‐physical properties (e.g., luminescence) have proven to be well suited to image proteins and peptides in living cells by various methods. An inherent advantage of metal complexes is the accessibility of multiple oxidation states, and overall charge and geometries, which makes them attractive from the point of view of chemical design. However, these properties can become a disadvantage if not controlled and fine‐tuned in the biological application. In this review, we generally discuss the use of metal compounds, targeting proteins and/or peptides, in medicinal chemistry and chemical biology, and then focus on representative recent examples and applications. Furthermore, we highlight future challenges and attractive perspectives in the field, which may stimulate research and define new frontiers in bioinorganic chemistry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".