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Updated: May 28, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
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A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

An Integrative Computational Pipeline for CK2 Inhibitor Discovery in Triple-Negative Breast Cancer Using Virtual

Abbas Khan1, Fahad M Alshabrmi2, Anwar Mohammad3

  • 1Department of Pharmaceutical Sciences, College of Pharmacy, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.

Pharmaceuticals (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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Researchers developed an AI framework to discover novel Casein Kinase II (CK2) inhibitors for triple-negative breast cancer (TNBC). This approach successfully identified potent natural compounds, offering new therapeutic strategies for this aggressive cancer subtype.

Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Bioinformatics

Background:

  • Triple-negative breast cancer (TNBC) is aggressive and lacks targeted therapies.
  • Casein Kinase II (CK2) is overexpressed in TNBC and is a promising drug target.

Purpose of the Study:

  • To identify potent CK2 inhibitors from natural product chemical space using an AI-integrated framework.
  • To establish a reproducible paradigm for de novo drug discovery in TNBC.

Main Methods:

  • AI-integrated virtual screening, molecular dynamics simulations, machine learning QSAR modeling, and quantum mechanical analysis.
  • Screening of ~480,000 natural compounds against CK2.
  • Biophysical validation including MM-PBSA/MM-GBSA and DFT analysis.
Keywords:
HOMO–LOMOQSARcasein kinase IImachine learningmolecular simulation

Related Experiment Videos

Last Updated: May 28, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Main Results:

  • Identified seven potent CK2 inhibitors from natural products, outperforming the reference inhibitor CX-4945.
  • Top compounds like Anastatin B and Rhein showed stable binding dynamics and favorable thermodynamic stability.
  • Machine learning QSAR models achieved high predictive performance (test R² ≈ 0.69) for inhibitor potency.

Conclusions:

  • The AI-integrated framework effectively bridges computational prediction and biophysical validation.
  • This study provides a reproducible method for discovering novel CK2 inhibitors for TNBC treatment.
  • The identified natural compounds represent promising leads for future drug development against TNBC.