Applications of KinTek Explorer Kinetic Simulations of Targeted Protein Degradation for Evaluation and Design of PROTACs
Bibliographic record
Abstract
Targeted protein degradation (TPD) technology centered on proteolysis-targeting chimeras (PROTACs) has become an increasingly transformative paradigm in drug discovery. PROTACs, by association with a disease-related target protein of interest and an E3 ligase, form a ternary complex in which the target protein undergoes subsequent ubiquitination and proteasomal degradation. This unique event-driven mechanism of action underscores the importance of kinetic simulation in facilitating the understanding of the kinetic parameters in TPD processes within a kinetic context to guide PROTAC design and optimization. Here, we employ KinTek Explorer to develop kinetic models for simulating PROTAC-induced ternary complex formation and the subsequent mechanistic steps leading to TPD. We illustrate the effects of, and interplay between, PROTAC binding specificity, affinity, cooperativity, and mechanism in complex TPD scenarios. Our findings highlight the effectiveness of KinTek Explorer in TPD kinetic simulation to facilitate PROTAC design.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".