ChemBioPort: an online portal to navigate the structure, function and chemical inhibition of the human proteome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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