Computational Precision Medicine Through Quantum-Enhanced Molecular Modeling

• Author(s): Chidi Ijeoma Nosiri; Paul Okoli; Echerou Leonard Nnadi; Shatange Dorothy Dooshima; Omaka Amblessed Chioma
• Paper ID: 1713178
• Page: 780-786
• Published Date: 31-07-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 1 July-2023
• DOI: https://doi.org/10.64388/IREV7I1-1713178

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.

Citations

IRE Journals:
Chidi Ijeoma Nosiri, Paul Okoli, Echerou Leonard Nnadi, Shatange Dorothy Dooshima, Omaka Amblessed Chioma "Computational Precision Medicine Through Quantum-Enhanced Molecular Modeling" Iconic Research And Engineering Journals Volume 7 Issue 1 2023 Page 780-786 https://doi.org/10.64388/IREV7I1-1713178

IEEE:
Chidi Ijeoma Nosiri, Paul Okoli, Echerou Leonard Nnadi, Shatange Dorothy Dooshima, Omaka Amblessed Chioma "Computational Precision Medicine Through Quantum-Enhanced Molecular Modeling" Iconic Research And Engineering Journals, vol. 7, no. 1, Jul. 2023, doi: https://doi.org/10.64388/IREV7I1-1713178

APA:
Chidi Ijeoma Nosiri, Paul Okoli, Echerou Leonard Nnadi, Shatange Dorothy Dooshima, Omaka Amblessed Chioma (2023). Computational Precision Medicine Through Quantum-Enhanced Molecular Modeling. Iconic Research And Engineering Journals, 7(1). doi: https://doi.org/10.64388/IREV7I1-1713178

MLA:
Chidi Ijeoma Nosiri, Paul Okoli, Echerou Leonard Nnadi, Shatange Dorothy Dooshima, Omaka Amblessed Chioma "Computational Precision Medicine Through Quantum-Enhanced Molecular Modeling" Iconic Research And Engineering Journals, vol. 7, no. 1, Jul. 2023. Crossref, https://doi.org/10.64388/IREV7I1-1713178

BibTeX

@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}
}