IN SILICO SCREENING OF MAJOR CANCER DRUG TARGETS (GROWTH FACTOR RECEPTORS) FOR NATURE DERIVED PHYTOCHEMICALS
Bibliographic record
Abstract
Cancer i s a group of abn ormal cells. The unregulated gro wth factor recepto r t yrosin e kinase ( GFR- TK) proteins are implicated in the proliferation o f mo re than 60 % o f all can cer t ypes. Screenin g o f ph ytoch emicals for their anti - angiogenic potential has b een a gro wing area o f research in the mod ern d ecad e. There i s a well - kno wn principle that natural co mpounds are active against several diseases, includin g variou s t yp es of can cer. Th e present research work fo cuses on kno wn gro wth factor receptors ( GFRs) as an important target fo r co mputation al s tudies. In this stud y, 96 curated anti - can cer co mpounds were virtu ally screened against the EGFR, FGFR, IGFR, and HGFR usin g molecular dockin g so ftware. For each GFR, we h ave considered ten top most results as potential hits. Among them, co mmon f i ve results are: Spiro solan e, Ginkgetin, Fangchinoline, Theaflavin and Ursolic acid. These co mpounds have b een reported to show antican cer activities in the l i t erature. With the help of different interaction analysis tools, the protein - l i gand inter action patterns between th e functional groups o f these co mpounds were analyzed. Hydro gen bonding and h ydrophobic forces are th e main co mponents o f th e interactions o f th ese hits, similar to those exp erimental fo r th e kno wn inhibitors. Fro m the maximu m nu mb ers o f hits, i t could be indicated that co mpounds Spirosolan e, Ginkgetin, Fan gchinoline, Th eaflavin and Ursolic acid are pro miscuous l ead s in the drug disco very process.
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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 | 0.000 |
| 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) | 0.001 | 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 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".