Integrating QSAR-Machine Learning, Biochemical Assays, and Molecular Dynamics for the Discovery of JAK2 Inhibitors in Cervical Cancer
Duangjai Todsaporn, Kamonpan Sanachai, Nattanit Suddee, Chompunud Kanjanapanyakom, Phornphimon Maitarad, Rattana Worayuthakarn, Chanat Aonbangkhen, Nopporn Thasana, Thanyada Rungrotmongkol · 2026
Read Integrating QSAR-Machine Learning, Biochemical Assays, and Molecular Dynamics for the Discovery of JAK2 Inhibitors in Cervical Cancer by Duangjai Todsaporn, Kamonpan Sanachai, Nattanit Suddee, Chompunud Kanjanapanyakom, Phornphimon Maitarad, Rattana Worayuthakarn, Chanat Aonbangkhen, Nopporn Thasana, Thanyada Rungrotmongkol (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13213829.txt.
Description
Duangjai Todsaporn and co-authors hunt for drugs that block a growth switch in cervical cancer, with three tools fused. The switch, JAK2, drives tumor growth when its path runs hot. Their 2026 study starts with a set of ring-based drafts, screened for kill skill in cervix tumor cells and for calm in plain cells. Two drafts block the switch at fine low doses and push tumor cells to die.
A learn tool then ranks fresh drafts by a trait-to-act map. Its top form calls the act marks near true in their tests. Flow mocks check how the best drafts sit on the switch. The fused path, bench plus code plus mock, can speed the hunt for a picky drug in a field with few.
Catalog record
| CREATOR | Duangjai Todsaporn, Kamonpan Sanachai, Nattanit Suddee, Chompunud Kanjanapanyakom, Phornphimon Maitarad, Rattana Worayuthakarn, Chanat Aonbangkhen, Nopporn Thasana, Thanyada Rungrotmongkol |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 72 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |