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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.ISTCESD038
Volume:
10 |
9 |
Sep 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Physics,
Indira Gandhi Senior College, CIDCO, New Nanded, Maharshtra, India.
Drug discovery is a computationally intensive, multistage pipeline encompassing target identification, molecular simulation, virtual screening, molecular docking, lead optimization, toxicity prediction, and experimental validation. Although classical computational tools have advanced the field, accurately modeling electronic structures and complex molecular interactions remains difficult due to the inherently quantum mechanical nature of subatomic physics. Quantum computing offers a alternative paradigm leveraging qubits, superposition, entanglement, and interference to overcome these limitations. This paper reviews the application of quantum computing across key drug discovery phases, including quantum chemistry, molecular dynamics, virtual screening, docking, quantum machine learning (QML), and generative molecular design. We discuss foundational algorithms and frameworks, such as the Variational Quantum Eigensolver (VQE), variational quantum circuits, quantum optimization, and hybrid quantum-classical workflows. Furthermore, we evaluate the critical constraints imposed by current Noisy Intermediate-Scale Quantum (NISQ) hardware—including high error rates, restricted qubit counts, fault-tolerant overhead, measurement costs, circuit depth limitations, and the challenge of proving quantum advantage over state-of-the-art classical baselines. Synthesis of current literature indicates that the most viable near-term trajectory is a hybrid architecture where quantum coprocessors target specific subproblems rather than an end-to-end quantum pipeline. Ultimately, the long-term impact of quantum computing on pharmaceutical discovery hinges on concurrent breakthroughs in fault-tolerant hardware, quantum algorithms, error correction, efficient data encoding, rigorous benchmarking, and seamless integration with existing computational chemistry and AI workflows.
KEYWORDS
fundamentally governed by quantum mechanics. Potential applications span quantum chemistry, molecular simulation, docking, virtual screening, molecular dynamics, quantum machine learning, and generative molecular design. VQE, QAOA, variational quantum circuits, and quantum neural networks provide important research directions. At the same time, current quantum computers remain limited by noise, qubit counts, error-correction overhead, measurement costs, and optimization difficulties. The available evidence therefore supports a cautious conclusion: quantum computing is not yet a general replacement for classical computational chemistry, but it is a serious research direction for selected problems where future hardware may deliver measurable advantages. Hybrid quantum-classical workflows are the most credible near-term path. The decisive milestone will be demonstrated quantum advantage on realistic, scientifically meaningful drug-discovery tasks rather than performance on isolated toy problems.
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Publication Date
9/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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