MolCom: a method to compare protein molecules based on 3-D structural and chemical similarity
Bibliographic record
Abstract
This paper describes an improved method for conducting global feature comparisons of protein molecules in three dimensions and for producing a new form of multiple structure alignment. Our automated MolCom method incorporates an octtree strategy to partition and examine molecular properties in three-dimensional space at multiple levels of analysis. The MolCom method's multiple alignment is in the form of an octtree which locates regions in three-dimensional space where correspondence between molecules is identified based on a dynamic set of molecular features. MolCom offers a practical solution to the inherent compromise between computational complexity and analytical detail. MolCom is currently the only method that can analyze and compare a series of defined physicochemical properties using multiple, simultaneous levels of resolution. It is also the only method that provides a consensus structure outlining precisely where the similarity exists in three-dimensional space. Using a modest-sized collection of structural properties, separate experiments were conducted to calibrate MolCom and to verify that the spatial analyses and resulting structure alignments accurately identified both similar and dissimilar structures. The accuracy of MolCom was found to be over 99% and the similarity scores correlated strongly with the z-scores of the Alignment by Incremental Combinatorial Extension of the Optimal Path method.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".