Chemical Proteomics-based Target Prioritization through a Residue Agnostic Ligandability Assessment Platform
Bibliographic record
Abstract
The landscape of drug discovery is undergoing a transformative phase with the influx of structural biology and omics data. Identifying optimal drug targets amid this data surge presents a multifaceted challenge. Covalent inhibitors, once undervalued, now hold substantial promise, especially targeted covalent inhibitors (TCIs), effectively engaging 'undruggable' proteins and overcoming resistance mechanisms. Existing ML software can proficiently model covalent ligands but lack comprehensive utility across large chemoproteomics sites. Challenges persist in predicting and assessing cryptic ligandable sites and sites beyond cysteine, demanding advanced computational tools. As cysteine-ligandable proteins represent only ~20% of the quantifiable proteome, there is a requirement for ligandability mapping of other nucleophilic amino acids. This study introduces a pioneering computational pipeline leveraging an AI-based ligandable predictor for meticulous evaluation of chemical proteomics-based reactive sites. The pipeline offers a scalable framework to assess covalent ligandability on a large scale, filter out improbable hits and systematically evaluate potential drug targets. Our work addresses covalent drug design challenges through a pipeline that fills crucial gaps in predicting cryptic ligandable and covalent sites in addition to cysteines to foster more efficient drug discovery methodologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".