The Druggable Genome as Seen from the Protein Data Bank
Bibliographic record
Abstract
Abstract Almost twenty years after the human genome was sequenced, the wealth of data produced by the international human genome project has not translated into a significantly improved drug discovery enterprise. This is in part because small molecule modulators that could be used to explore the cellular function of their target proteins and to discover new therapeutic opportunities are only available for a limited portion of the human proteome. International efforts are underway to develop such chemical tools for a few, specific protein families, and a “Target 2035” call to enable, expand and federate these efforts towards a comprehensive chemical coverage of the druggable genome was recently announced. But what is the druggable genome? Here, we systematically review structures of human proteins bound to drug-like ligands available from the protein databank (PDB) and use ligand desolvation upon binding as a druggability metric to draw a landscape of the human druggable genome. We show that the vast majority of druggable protein families, including some highly populated and deeply associated with cancer according to genomic screens, are almost orphan of small molecule ligands, and propose a list of 46 druggable domains representing 3440 human proteins that could be the focus of large chemical probe discovery efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".