Computational Precision Medicine Through Quantum-Enhanced Molecular Modeling

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@article{1713178,
      author = {Chidi Ijeoma Nosiri, Paul Okoli, Echerou Leonard Nnadi, Shatange Dorothy Dooshima, Omaka Amblessed Chioma},
      title = {Computational Precision Medicine Through Quantum-Enhanced Molecular Modeling},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {780-786},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713178.pdf},
      abstract = {The exorbitant costs, protracted timelines, and elevated attrition rates associated with traditional drug-discovery pipelines underscore the pressing necessity for computational frameworks that can elucidate biomolecular interactions with quantum-level precision and practical scalability. This study introduces a Hybrid Quantum Classical (HQC) framework that combines quantum variational algorithms, classical molecular dynamics, and quantum-assisted machine-learning optimization into a single drug-screening workflow. It builds on the basic idea of Quantum Molecular Simulation (QMS). In the suggested design, quantum processors are used to selectively fix high-fidelity electronic interactions in reactive binding sites, while classical engines model the large-scale conformational dynamics of biomolecular environments. An adaptive quantum-machine-learning layer speeds up convergence even more by learning how structure and energy are related from quantum-refined descriptors. Benchmark tests against oncogenic targets like EGFR, BCR-ABL1, and p53 show that our method is up to 27% more accurate at converging binding energy and takes 4.3 times less time to compute than standard density-functional-theory and standalone QMS methods. The HQC framework makes quantum drug simulation possible for real-world drug discovery by reducing the limitations of current quantum hardware while keeping electronic-scale accuracy. This study demonstrates that hybrid quantum-classical modeling is a scalable and hardware-compatible approach for advancing next-generation computational drug design and precision therapeutics.},
      keywords = {Hybrid Quantum?Classical Computing; Quantum Molecular Simulation; Drug Discovery; Variational Quantum Eigensolver (VQE); Quantum Approximate Optimization Algorithm (QAOA); Quantum Machine Learning; QM/MM Modeling; Molecular Dynamics; Binding-Energy Prediction.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV7I1-1713178}
  }