The protein–small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding
Bibliographic record
Abstract
MOTIVATION: An enabling resource for drug discovery and protein function prediction is a large, accurate and actively maintained collection of protein/small-molecule complex structures. Models of binding are typically constructed from these structural libraries by generalizing the observed interaction patterns. Consequently, the quality of the model is dependent on the quality of the structural library. An ideal library should be non-biased and comprehensive, contain high-resolution structures and be actively maintained. RESULTS: We present a new protein/small-molecule database (the PSMDB) that offers a non-redundant set of holo PDB complexes. The database was designed to allow frequent updates through a fully automated process without manual annotation or filtering. Our method of database construction addresses redundancy at both the protein and the small-molecule level. By efficiently handling structures with covalently bound ligands, we allow our database to include a larger number of structures than previous methods. Multiple versions of the database are available at our web site, including structures of split complexes--the proteins without their binding ligands and the non-covalently bound ligands within their native coordinate frame. AVAILABILITY: http://compbio.cs.toronto.edu/psmdb
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".