Rational Chemical Design of Molecular Glue Degraders
Bibliographic record
Abstract
Abstract Targeted protein degradation with molecular glue degraders has arisen as a powerful therapeutic modality for eliminating classically undruggable disease-causing proteins through proteasome-mediated degradation. However, we currently lack rational chemical design principles for converting protein-targeting ligands into molecular glue degraders. To overcome this challenge, we sought to identify a transposable chemical handle that would convert protein-targeting ligands into molecular degraders of their corresponding targets. Using the CDK4/6 inhibitor Ribociclib as a prototype, we identified a covalent handle that, when appended to the exit vector of Ribociclib, induced the proteasome-mediated degradation of CDK4 in cancer cells. Covalent chemoproteomic profiling of this CDK4 degrader revealed covalent interactions with cysteine 32 of the RING family E3 ubiquitin ligase RNF126. Structural modification of our initial covalent scaffold led to an improved CDK4 degrader with the development of a but-2-ene, 1,4-dione ( “ fumarate ” ) handle that showed improved interactions with RNF126. Thereafter, we worked to identify the minimum covalent motif required for interaction with RNF126, which we then transplanted onto chemically related and un-related protein-targeting ligands. This strategy successfully produced molecules which induced the degradation of several proteins across diverse protein classes, including BRD4, BCR-ABL and c-ABL, PDE5, AR and AR-V7, BTK, LRRK2, and SMARCA2. Our study undercovers a design strategy for converting protein-targeting ligands into covalent molecular glue degraders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".