A Loop‐Based and AGO‐Incorporated Virtual Screening Model Targeting AGO‐Mediated miRNA–mRNA Interactions for Drug Discovery to Rescue Bone Phenotype in Genetically Modified Mice
Why this work is in the frame
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Bibliographic record
Abstract
Several virtual screening models are proposed to screen small molecules only targeting primary miRNAs without selectivity. Few attempts have been made to develop virtual screening strategies for discovering small molecules targeting mature miRNAs. Mature miRNAs and their specific target mRNA can form unique functional loops during argonaute (AGO)-mediated miRNA-mRNA interactions, which may serve as potential targets for small-molecule drug discovery. Thus, a loop-based and AGO-incorporated virtual screening model is constructed for targeting the loops. The previously published studies have found that miR-214 can target ATF4 to inhibit osteoblastic bone formation, whereas miR-214 can target TRAF3 to promote osteoclast activity. By using the virtual model, the top ten candidate small molecules targeting miR-214-ATF4 mRNA interactions and top ten candidate small molecules targeting miR-214-TRAF3 mRNA interactions are selected, respectively. Based on both in vitro and in vivo data, one small molecule can target miR-214-ATF4 mRNA to promote ATF4 protein expression and enhance osteogenic potential, whereas one small molecule can target miR-214-TRAF3 mRNA to promote TRAF3 protein expression and inhibit osteoclast activity. These data indicate that the loop-based and AGO-incorporated virtual screening model can help to obtain small molecules specifically targeting miRNA-mRNA interactions to rescue bone phenotype in genetically modified mice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it