Advances in covalent kinase inhibitors
Bibliographic record
Abstract
Over the past decade, covalent kinase inhibitors (CKI) have seen a resurgence in drug discovery. Covalency affords a unique set of advantages as well as challenges relative to their non-covalent counterpart. After reversible protein target recognition and binding, covalent inhibitors irreversibly modify a proximal nucleophilic residue on the protein via reaction with an electrophile. To date, the acrylamide group remains the predominantly employed electrophile in CKI development, with its incorporation in the majority of clinical candidates and FDA approved covalent therapies. Nonetheless, in recent years considerable efforts have ensued to characterize alternative electrophiles that exhibit irreversible or reversibly covalent binding mechanisms towards cysteine thiols and other amino acids. This review article provides a comprehensive overview of CKIs reported in the literature over a decade period, 2007-2018. Emphasis is placed on the rationale behind warhead choice, optimization approach, and inhibitor design. Current FDA approved CKIs are also highlighted, in addition to a detailed analysis of the common trends and themes observed within the listed data set.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".