Click. Screen. Degrade. A Miniaturized D2B Workflow for rapid PROTAC Discovery
Bibliographic record
Abstract
SUMMARY Targeted protein degradation is one of the fastest developing fields in medicinal chemistry and chemical biology. Despite significant development in assay technologies and inhibitor discovery, the development of PROTACs remains a challenging endeavor since rational design approaches remain widely elusive. Our workflow eliminates the rate-limiting step of classic synthesis, namely compound purification, and pairs it with high-throughput, semi-automated plate-based synthesis, and direct cellular assay evaluation. We applied this direct-to-biology approach to four diverse targets demonstrating the general applicability of this technology. PROTAC synthesis was realized by using the highly efficient copper-catalyzed azide-alkyne cycloaddition reaction. This simplified reaction setup allowed synthesis in the nanomole scale with reaction volumes as low as 5 μL. This high throughput approach enables the synthesis and testing of hundreds of PROTACs within a few days, allowing for a comprehensive assessment of target degradability and the identification of the most suitable E3 ligase for degrader development.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.053 |
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".