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

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.