Comprehensive in silico discovery of c-Src tyrosine kinase inhibitors in cancer treatment: A unified approach combining pharmacophore modeling, 3D QSAR, DFT, and molecular dynamics simulation
Bibliographic record
Abstract
To investigate c-Src, a non-receptor tyrosine kinase dysregulated in various cancer types including colon, breast, and pancreatic cancers, as a potential drug target for cancer therapy. Ligand-based pharmacophore modeling and 3D-QSAR analysis on a dataset of 34 c-Src tyrosine kinase inhibitors were employed. The established pharmacophore model (DDRRR_1) features two hydrogen bond donor (D) and three aromatic ring (R) features, exhibiting favorable parameters (R2 = 0.926; Q2 = 0.895; F value = 47.9). Hypothesis validation, enrichment analysis, and contour plot analysis were conducted, followed by virtual screening of a PubChem database using the optimized pharmacophore model and filtering based on the Lipinski rule of five. The most promising inhibitors underwent multistep molecular docking, density Functional Theory (DFT) analysis, ADMET assessments, molecular dynamics simulation, and PCA. CID_70144047 emerged as the most promising hit with all the above favorable properties. The study provides a comprehensive approach for identifying novel c-Src tyrosine kinase inhibitors, highlighting CID_70144047 as a promising leads with potential therapeutic applications in cancer treatment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".