Application of Weighted Interaction-Fingerprints for Rationalizing Neosubstrate Potency and Selectivity of Cereblon-Based Molecular Glues
Bibliographic record
Abstract
Abstract Cullin-RING Ligase 4 Cereblon (CRL4CRBN) (CRBN) E3 ligase modulatory drugs (CELMoDsTM) make up a successful class of compounds targeting neosubstrates for proteasome-dependent degradation. Early immunomodulatory drugs (IMiDsTM) target Ikaros and Aiolos degradation. In addition, there are ongoing clinical trials targeting the degradation of biologically relevant proteins such as GSPT1, CK1α, and Helios with CRBN-based molecular glues. To date, most advanced preclinical and clinical CRBN-based molecular glues recruit their neosubstrates through canonical G-motifs, secondary protein features that are structurally similar but have significantly different amino acid sequence identities. Analogous to the development of kinase inhibitors, optimizing both neosubstrate recruitment and degradation selectivity is important to minimize potential off-target activity. Here, we describe a computational structure-based approach to analyze and predict putative ligand interactions important in the neosubstrate ternary complex. This approach provides valuable insights for enhanced designs toward the development of more selective and efficacious CRBN-based molecular glues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".