A DNA‐Encoded Library of Chemical Compounds Based on Common Scaffolding Structures Reveals the Impact of Ligand Geometry on Protein Recognition
Bibliographic record
Abstract
Abstract A DNA‐encoded chemical library (DECL) with 1.2 million compounds was synthesized by combinatorial reaction of seven central scaffolds with two sets of 343×492 building blocks. Library screening by affinity capture revealed that for some target proteins, the chemical nature of building blocks dominated the selection results, whereas for other proteins, the central scaffold also crucially contributed to ligand affinity. Molecules based on a 3,5‐bis(aminomethyl)benzoic acid core structure were found to bind human serum albumin with a Kd value of 6 nm, while compounds with the same substituents on an equidistant but flexible l‐lysine scaffold showed 140‐fold lower affinity. A 18 nm tankyrase‐1 binder featured l‐lysine as linking moiety, while molecules based on d‐Lysine or (2S,4S)‐amino‐l‐proline showed no detectable binding to the target. This work suggests that central scaffolds which predispose the orientation of chemical building blocks toward the protein target may enhance the screening productivity of encoded libraries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".