In Silico Study of Mangosteen Fruit (Garcinia mangostana L.) as Pancreatic Anticancer Against AKT Kinase
Bibliographic record
Abstract
Keganasan pada pankreas diakibatkan karena tidak terkendalinya proliferasi sel yang akan mengarahpada berkurangnya proses apoptosis sel. Oleh karena itu, salah satu usaha untuk menangani kasuskanker pankreas ini adalah dengan menghambat protein antiapoptosis seperti AKT kinase. Inhibisiprotein AKT kinase didasarkan pada mekanisme penghambatan fosforilasi protein AKT kinase padasel pankreas. Senyawa golongan xanton dari buah manggis (Garcinia mangostana L.) telah terbuktimemiliki aktivitas antikanker. Pada penelitian ini senyawa golongan santon diteliti potensinya sebagaiobat anti kanker pankreas melalui metode penambatan molekuler yang dilakukan terhadap reseptorprotein AKT kinase dengan bantuan AutoDockTools (versi 4,2.6 dan 1.5.6), divisualisasikan denganBIOVIA Discovery Studio 2020, lalu analisis profil farmakokinetika serta toksisitas dan drug-likenessyang mengacu pada Lipinski’s Rule of Five melalui situs web pre-ADMET. Penambatan molekulermenghasilkan 5 senyawa turunan santon yang memenuhi kriteria. Senyawa 1-isomangostin memilikipotensi aktivitas yang lebih baik jika dibandingkan dengan ligan pembanding dan ligan uji lainnyadengan energi ikatan (ΔG) -9,90 kkal/mol dan konstanta inhibisi 54,9 nM. Hasil prediksi ADMET dari1-isomangostin menunjukkan sifat absorpsi yang baik (HIA = 94%, Caco2 = 39,97), distribusi yangbaik (PPB = 79,1%), tidak mampu menembus sawar otak (BBB = 0,179), dan tidak bersifat toksik.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".