Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data

Don Roosan, Saif Nirzhor, Rubayat Khan, Fahmida Hai

2025

Abstract

This paper presents a novel quantum-enhanced prototype for drug repurposing and addresses the challenge of managing massive genomics data in precision medicine. Leveraging cutting-edge quantum server architectures, we integrated quantum-inspired feature extraction with large language model (LLM)–-based analytics and unified high-dimensional omics datasets and textual corpora for faster and more accurate therapeutic insights. Applying Synthetic Minority Over-sampling Technique (SMOTE) to balance underrepresented cancer subtypes and multi-omics sources such as TCGA and LINCS, the pipeline generated refined embeddings through quantum principal component analysis (QPCA). These embeddings drove an LLM trained on biomedical texts and clinical notes, generating drug recommendations with improved predicted efficacy and safety profiles. Combining quantum computing with LLM outperformed classical PCA-based approaches in accuracy, F1 score, and area under the ROC curve. Our prototype highlights the potential of harnessing quantum computing and next-generation servers for scalable, explainable, and timely drug repurposing in modern healthcare.

Paper Citation

in Harvard Style

Roosan D., Nirzhor S., Khan R. and Hai F. (2025). Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 465-472. DOI: 10.5220/0013524900003967

in Bibtex Style

@conference{data25,
author={Don Roosan and Saif Nirzhor and Rubayat Khan and Fahmida Hai},
title={Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={465-472},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013524900003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data
SN - 978-989-758-758-0
AU - Roosan D.
AU - Nirzhor S.
AU - Khan R.
AU - Hai F.
PY - 2025
SP - 465
EP - 472
DO - 10.5220/0013524900003967
PB - SciTePress