Docking-based Virtual Screening for the Discovery of RNA-targeting Molecules: Identification of Selective Riboswitch Binding Ligands
Bibliographic record
Abstract
We present herein one of the very first applications of molecular docking towards the discovery of novel RNA-targeting molecules. Our in-house docking program FITTED was used to perform independ-ent virtual screening campaigns against the TPP, SAM, and FMN riboswitches. Based on the predicted docking scores and poses, between 14 and 20 compounds were selected from commercial libraries and purchased for experimental evaluation of binding to their respective riboswitches by surface plasmon resonance. Promisingly, two compounds displayed highly specific and dose-dependent binding to the TPP riboswitch and FMN riboswitch, with experimentally-determined KDs of 170 μM and 220 μM, re-spectively. This work highlights the promise of modifying and applying docking programs for the dis-covery of nucleic acid- targeting molecules.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.000 |
| 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".