COMPUTATIONAL ANALYSIS OF TRANSCRIPTION FACTORS AS CANCER DRUG TARGETS WITH POTENTIAL INHIBITORS FROM THE NPACT DATABASE
Bibliographic record
Abstract
Transcription factors have proven to be promising targets for the treatment of cancer. Transcription factors are involved in the production of oxygen. External cervical events are initiated by receptors, such as cytotoxic exposures or cytokine receptors that trigger signalling cascades that activate transcription factors. Transcriptional factors are known to be highly active in most human cancer cells, making them suitable for the study and development of anticancer therapies. Three transcription factors were investigated as potential targets in this study. Analysis of string interactions reveals their interaction network. DNA-TF binding was followed by docking with 96 natural compounds to the DNA binding pocket of the transcription factor. Using post-docking processing, compounds were ranked according to their binding energy, hydrogen bond number, and dissociation constant; Withanolide D targeted more than one transcription factor. Therefore, the compound is suitable for in vitro testing using different cancer cell lines.
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.
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 | 0.000 |
| 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 | 0.000 |
| 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".