From fragments to follow-ups: rapid hit expansion by making use of EU-OPENSCREEN resources
Bibliographic record
Abstract
Quite frequently, it is the progression of initial crystallographic fragment screening hits into more potent binders to their target, which constitutes the major bottleneck in many academic compound or drug development projects. While high quality starting points are critical to the success of a drug development project, it is equally important to have accessible pathways for further compound development. Here, we present two crystallographic fragment screening campaigns using a 96 fragment sub-selection of the European Fragment Screening Library (EFSL) provided by EU-OPENSCREEN. The two campaigns against the targets endothiapepsin and the NS2B–NS3 Zika protease, yielded hit rates of 31% and 18%, respectively. Further, we present how within the framework of the EU-OPENSCREEN European Research Infrastructure Consortium (ERIC) fast identification of follow-up compounds can be realized. With just one round of testing related compounds from the European Chemical Biology Library, two follow-up binders for each of the two targets could be identified proving the feasibility of this approach.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".