Thiol-Dependent Enzymes and Their Inhibitors: A Review
Bibliographic record
Abstract
Biological thiol-dependent enzymes have recently received extensive attention in the literature because of their involvement in a variety of physiopathological conditions. The active thiol groups of these enzymes are derived from the cysteine residues present. Hence, in a biological system, the selective reversible or irreversible inhibition of the activity of these enzymes by modification of the thiol moiety may potentially lead to the development of a chemotherapeutic treatment. Despite all the research efforts involved in the attempt to develop potential chemotherapeutic treatments for the major diseases involving cysteine proteases, there are in fact no such treatments available yet. However, AG7088 (1) an inhibitor of rhinovirus-3C is in phase II/III clinical trial for the treatment of common cold and VX-740 (2, pralnacasan) an inhibitor of caspase-1 is in phase II clinical trial as an anti-inflammatory agent for rheumatoid arthritis. Several other cysteine protease inhibitors (i.e., cathepsin K, and S) are in pre-clinical evaluation or pre-clinical development. Structure-based drug design approaches have been instrumental in the development of these inhibitors. Intensive biochemical studies on the cysteine proteases have shed some light on some potential targets for therapeutic development. In addition, new techniques and new ideas are constantly emerging. As such, an up-to-date review of the literature on thiol-dependent enzymes as potential targets and their inhibitors designed from peptidic, modified peptidomimetic scaffolds and from small heterocyclic molecules is presented.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".