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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".