ChemBioPort: an online portal to navigate the structure, function and chemical inhibition of the human proteome
Bibliographic record
Abstract
Chemical probes are important tools to investigate the function of proteins, evaluate their potential as therapeutic targets and provide chemical starting points for drug discovery. As a result, a growing federation of scientists aims to generate chemical probes for all human druggable proteins. A diverse array of data typically guides target selection and chemical probe discovery: information on protein function can help prioritize targets, domain architecture can provide insight on druggability, structural data enables molecular design and existing chemical ligands can serve as foundation or inspiration for chemical probe development. But these heterogenous data types are dispersed across a variety of public repositories that are difficult to cross-reference by non-experts. We developed ChemBioPort, an online resource that allows users to combine queries related to the ontology, domain architecture or name of human proteins to produce downloadable tables that integrate information on function, disease association, essentiality, tissue enrichment, domain architecture, structure and chemical ligands of proteins. Users can convert these tables into dendrograms reflecting sequence similarity, onto which they can graphically project all data types, linked via a mouse-click to their original repositories or published articles. This interface will support the growing community of chemical biologists, chemists, cell and structural biologists on their perilous journey from genes to medicines. Database URL: https://chembioport.thesgc.org.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.148 | 0.077 |
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".