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PreprintCC BYChemRxiv

Quantum Graph-Theoretic Docking of Multiple Ligands Targeting CMG Helicase: Analysis of Interaction Patterns and Ideal Ligand Composition

  • Marina Gavrilovskaia1,2,3,4
  • Katarzyna Mizgalska1,2,5,4
  • Timothy H. Tran6,2
  • Wayne C. Guida5,4
  • Nicholas J. Lawrence7,2,8,4
  • Harshani R. Lawrence6,2,7,8,4
  • Mark G. Alexandrow8,4,9,2,10
  • Kwang-Cheng Chen3,4
  • Aleksandra Karolak1,2,8,4
10 affiliations
  1. 1Department of Machine Learning
  2. 2Moffitt Cancer Center and Research Institute
  3. 3Department of Electrical Engineering
  4. 4University of South Florida
  5. 5Department of Chemistry
  6. 6Chemical Biology Core Facility
  7. 7Drug Discovery Department
  8. 8Department of Oncologic Sciences
  9. 9Molecular Oncology Department
  10. 10Cancer Biology and Evolution Program
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Abstract

Molecular docking and compound screening can overlook and biochemically relevant binding configurations of molecules, limiting the ability to identify true functional poses of potential drug candidates. To address this, we apply the Quantum Approximate Optimization Algorithm (QAOA) to improve the evaluation of target–ligand interaction patterns. As a proof of concept, we focus on the CDC45–MCM2-7–GINS (CMG) helicase, which is essential for DNA replication and represents a potential therapeutic target in cancer due to mutant KRas-induced vulnerabilities in the CMG and its MCM hexameric core. In this study, docking of three ligands: methylbiocin, novobiocin, and BL_C43, to one of the ATP-binding regions of the CMG was formulated as a combinatorial optimization problem. Pharmacophore interactions were encoded in a Binding Interaction Graph and mapped to a Maximum Independent Set problem, allowing exploration of all feasible docking configurations. The quantum workflow identified ligand-dependent interaction patterns and biologically relevant binding orientations that classical docking methods failed to predict, demonstrating superior agreement with experimental biochemical outcomes for CMG inhibitors. These findings establish QAOA as a promising approach for overcoming limitations of classical molecular docking and advancing the rational design of improved inhibitors targeting CMG and other cancer-associated proteins.

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Cite

BibTeXRIS
BibTeX
@misc{gavrilovskaia2026quantum,
  title = {{Quantum Graph-Theoretic Docking of Multiple Ligands Targeting CMG Helicase: Analysis of Interaction Patterns and Ideal Ligand Composition}},
  author = {Marina Gavrilovskaia and Katarzyna Mizgalska and Timothy H. Tran and Wayne C. Guida and Nicholas J. Lawrence and Harshani R. Lawrence and Mark G. Alexandrow and Kwang-Cheng Chen and Aleksandra Karolak},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010835/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010835/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Quantum Graph-Theoretic Docking of Multiple Ligands Targeting CMG Helicase: Analysis of Interaction Patterns and Ideal Ligand Composition
AU  - Marina Gavrilovskaia
AU  - Katarzyna Mizgalska
AU  - Timothy H. Tran
AU  - Wayne C. Guida
AU  - Nicholas J. Lawrence
AU  - Harshani R. Lawrence
AU  - Mark G. Alexandrow
AU  - Kwang-Cheng Chen
AU  - Aleksandra Karolak
PY  - 2026
DA  - 2026/10/22
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010835/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010835/v1
AB  - Molecular docking and compound screening can overlook and biochemically relevant binding configurations of molecules, limiting the ability to identify true functional poses of potential drug candidates. To address this, we apply the Quantum Approximate Optimization Algorithm (QAOA) to improve the evaluation of target–ligand interaction patterns. As a proof of concept, we focus on the CDC45–MCM2-7–GINS (CMG) helicase, which is essential for DNA replication and represents a potential therapeutic target in cancer due to mutant KRas-induced vulnerabilities in the CMG and its MCM hexameric core. In this study, docking of three ligands: methylbiocin, novobiocin, and BL_C43, to one of the ATP-binding regions of the CMG was formulated as a combinatorial optimization problem. Pharmacophore interactions were encoded in a Binding Interaction Graph and mapped to a Maximum Independent Set problem, allowing exploration of all feasible docking configurations. The quantum workflow identified ligand-dependent interaction patterns and biologically relevant binding orientations that classical docking methods failed to predict, demonstrating superior agreement with experimental biochemical outcomes for CMG inhibitors. These findings establish QAOA as a promising approach for overcoming limitations of classical molecular docking and advancing the rational design of improved inhibitors targeting CMG and other cancer-associated proteins.
ER  -