PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 7, 2026Journal of Chemical Information and Modeling0 citationsOpen Access

Integrating QSAR-Machine Learning, Biochemical Assays, and Molecular Dynamics for the Discovery of JAK2 Inhibitors in Cervical Cancer

View Full Paper
DTDuangjai TodsapornKSKamonpan SanachaiNSNattanit Suddee

Key Points

  • The aim is to discover JAK2 inhibitors for cervical cancer using integrated computational approaches.
  • Used QSAR-Machine Learning to identify potential JAK2 inhibitors.
  • Conducted biochemical assays for drug-likeness evaluation.
  • Performed 1-μs molecular dynamics simulations to study inhibitor interactions.
  • Identified promising naphthalene-based scaffolds exhibiting potent nanomolar JAK2 inhibition.
  • Molecular dynamics simulations revealed critical hydrogen bonding interactions and hydrophobic contacts that stabilize inhibitors.

Abstract

meeting drug-likeness criteria and displaying potent nanomolar JAK2 inhibition. Finally, 1-μs molecular dynamics simulations revealed that hydrophobic contacts and hydrogen bonding cooperatively stabilize these inhibitors within the JAK2 ATP-binding pocket. Collectively, these findings establish a QSAR-ML-guided strategy for accelerating JAK2 inhibitor discovery and highlight naphthalene-based scaffolds as promising leads for targeted cervical cancer therapy.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Todsaporn et al. (2026) studied this question.

synapsesocial.com/papers/69fbefc0164b5133a91a3c61https://doi.org/10.1021/acs.jcim.6c00414
Ask AI
Helpful
Bookmark
Share
View Full Paper