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

doi:10.1021/acs.jcim.6c00414

May 5, 2026Journal of Chemical Information and ModelingOpen Access

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

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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.

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Implication

Study demonstrates potent JAK2 inhibition in cervical cancer using molecular dynamics and hydrogen bonding, highlighting a new strategy for therapy.

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.

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Todsaporn et al. (2026) studied this question.

synapsesocial.com/papers/69fbefc0164b5133a91a3c61 https://doi.org/10.1021/acs.jcim.6c00414

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

Key Points

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Abstract

Discussion

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

Key Points

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Abstract

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