Multiplexed Target Profiling with Integrated Chemical Genomics and Chemical Proteomics
Bibliographic record
Abstract
Target identification is crucial for elucidating the mechanisms of bioactive molecules in drug discovery. However, traditional methods assess compounds individually, making it challenging to efficiently examine multiple compounds in parallel, especially for structurally diverse compounds. This study reports a novel strategy called chemical genomics-facilitated chemical proteomics (CGCP) for multiplexing the target identification of bioactive small molecules. CGCP correlates compounds' perturbation of global transcription, or chemical genomic profiles, with their reactivity toward target proteins, enabling simultaneous identification of targets. We demonstrated the utility of CGCP by studying the targets of celastrol (Cel) and four other electrophilic compounds with varying levels of similarity to Cel based on their chemical genomic profiles. We identified multiple novel targets and binding sites shared by the compounds in a single experiment. CGCP enabled multiplexity and improved the efficiency of target identification for structurally distinct compounds, indicating its potential to accelerate drug discovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".