Developing Inhibitors of STAT3: Targeting Downstream of the Kinases for Treating Disease
Bibliographic record
Abstract
Over the past 20 years, drug discovery programs focusing on the aberrant disease-related proteins of the kinome have resulted in some of the most clinically significant small molecules. The enormous effort and success in producing potent inhibitors against these targets has laid the groundwork for more recent research into the design of inhibitors against more nontraditional targets of human disease. One protein of interest is the signal transducer and activator of transcription 3 (STAT3) that has been shown to play a central role in the progression of numerous diseases, including cancer, inflammatory disease and Alzheimer's disease, and is described as overactive in nearly 70% of all solid and hematological malignancies. This chapter first describes STAT3 structure and signaling and then focuses on directly targeting the STAT3 protein. Small molecules derived from natural sources are an important class of compounds to be utilized for the treatment of disease.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".