Images Based System for Surface Matching in Macromolecular Screening
Why this work is in the frame
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Bibliographic record
Abstract
Computer vision technologies of structure matching based on surface representation have demonstrated their effectiveness in many research fields. In particular they can be successfully applied to in silico studies of molecular biology. Protein activities, in fact, are driven by their external characteristics, therefore the ability to match surfaces allows one to quickly infer information about possible interactions and functions of biological components.In this work we present a surface matching algorithm which is able to screen possible macromolecular interactions in terms of surface complementarities. The main characteristics of the algorithm is the exploitation of an intermediate level of data representation for 3D surfaces based on images of local description. This approach enables the matching of small pieces of surfaces, which is a crucial aspect when working in the biological context.
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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 | 0.000 |
| 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) | 0.000 | 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