Ligand Specificity in Fragment-Based Drug Design
Bibliographic record
Abstract
Fragment-based drug design consists of identifying low-molecular weight compounds that weakly bind to a target macromolecule and will then be modified or linked to yield potent inhibitors. The specificity of these low-complexity and low-affinity molecules has rarely been discussed in the literature. To address this question, NMR spectroscopy was used to investigate the interactions of 150 fragments with five proteins: three proteins from the Bcl-2 family (Bcl-x(L), Bcl-w, and Mcl-1), human peroxiredoxin 5, for which very few ligands have been reported, and human serum albumin, which is known to bind a large number of ligands. Our results show that the fragments are rather versatile binders and able to identify binding hot spots in very different targets. Despite the different hit rates observed related to the druggability of the proteins, two scaffolds appear as preferred binders for all proteins. Low specificity was observed between homologous proteins or unrelated poorly druggable proteins, while higher specificity could be achieved with highly druggable targets.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".