Structure-Based In Silico Investigation of Agonists for Proteins Involved in Breast Cancer
Bibliographic record
Abstract
Cancer is recognized as one of the main causes of mortality worldwide by the World Health Organization. The high cost of currently available cancer therapy and certain limitations of current treatment make it necessary to search for novel, cost-effective, and efficient methods of cancer treatment. Therefore, in the current investigation, sixty-two compounds from five medicinal plants (Tinospora cordifolia, Ocimum tenuiflorum, Podophyllum hexandrum, Andrographis paniculata, and Beta vulgaris) and two proteins that are associated with breast cancer, i.e., HER4/ErbB4 kinase and ERα were selected. Selected compounds were screened using Lipinski’s rule, which resulted in eighteen molecules being ruled out. The remaining forty-four compounds were then taken forward for docking studies followed by molecular dynamics studies of the best screened complexes. Results showed that isocolumbin, isopropylideneandrographolide, and 14-acetylandrographolide were potential lead compounds against the selected breast cancer receptors. Furthermore, in vitro studies are required to confirm the efficacy of the lead compounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".