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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.
Abstract:
Background: Triple-negative breast cancer (TNBC) remains among the most aggressive and therapeutically unresponsive subtypes due to the absence of ER, PR, and HER2 targets. Casein Kinase II (CK2), a pleiotropic serine/threonine kinase overexpressed in TNBC, represents a compelling target for rational drug design. Methods: Here, we present an AI-integrated benchmarking framework combining virtual drug discovery, molecular dynamics simulations, machine learning-driven QSAR modeling, and quantum-mechanical electronic structure analysis to identify potent CK2 inhibitors from natural product chemical space. Results: A validated XP docking protocol (ROC-AUC = 0.748) screened ~480,000 compounds, yielding seven hits, with superior binding to the reference inhibitor CX-4945. Among these, Anastatin B, 3,4,8,9,10-pentahydroxy-dibenzo-[b,d]pyran-6-one, Rhein, and aloe emodin acetate exhibited highly favorable docking scores (-11.6 to -13.1 kcal mol-1) and stable 200 ns binding dynamics, reflected by RMSD ≤ 2 Å and compact Rg trajectories. MM-PBSA/MM-GBSA analyses confirmed robust thermodynamic stability, while DFT-derived HOMO-LUMO gaps (3.8-4.3 eV) suggested optimal electronic reactivity for kinase inhibition. Machine learning QSAR models demonstrated strong predictive performance, with the best stacking models achieving test R2 ≈ 0.69 and consistent cross-validation performance (CV R2 ≈ 0.66-0.69), supporting reliable prediction of pIC50 values and prioritization of top-ranked scaffolds. Conclusions: Collectively, this integrative framework bridges AI-based learning and biophysical validation, establishing a reproducible paradigm for de novo CK2 inhibitor discovery in TNBC.
Insights
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.
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.