Authors:
Subrahmanya Bhat, Sreeja Rajesh, T. Shreekumar

Addresses:
Faculty in Department of Information Science and Engineering, Mangalore Institute of Technology and Engineering,  Mijar, Moodabidri, Karnataka, India. Faculty in Department of Computer Science and Engineering, Mangalore Institute of Technology and Engineering, Mijar, Moodabidri, Karnataka, India.

Abstract:

The increasing concern of multidrug-resistant bacterial and fungal pathogens has raised an urgent need for more efficient and accurate drug discovery strategies. Conventional strategies are becoming less effective in addressing the increasing complexity of microbial resistance development. In this article, a quantum-assisted bioinformatics platform is introduced that integrates quantum computing, artificial intelligence (AI), explainable AI (xAI), and blockchain technology to simplify the identification of new anti-bacterial and anti-fungal organic compounds. Quantum algorithms, such as the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE), are utilised to model the accurate interactions between potential compounds and target microbial proteins. Machine learning models create and refine novel molecular structures, whereas xAI provides interpretability of predictions by identifying molecular features associated with activity and toxicity. High-throughput screening and in vivo assays confirm the efficacy, pharmacokinetics, and safety of the compound. An ongoing feedback loop refines compound structures based on real-world performance, and blockchain enables secure, tamper-proof collaboration between institutions. This interdisciplinary solution not only enhances the accuracy of predictions and shortens development time but also improves transparency, collaboration, and personalisation in the battle against antimicrobial resistance.

Keywords: Quantum Computing; Anti-Bacterial Compounds; Anti-Fungal Drug Discovery; Artificial Intelligence; Explainable AI; Blockchain Technology; Quantum Phase Estimation (QPE); Variational Quantum Eigensolver (VQE).

Received on: 02/10/2024, Revised on: 11/12/2024, Accepted on: 24/01/2025, Published on: 05/06/2025

DOI: 10.69888/FTSHSL.2025.000461

FMDB Transactions on Sustainable Health Science Letters, 2025 Vol. 3 No. 2, Pages: 92-100

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