Accelerating Drug Development: The Role of Quantum Computing in Pharmaceutical Research

DOI

10.2991/978-94-6463-813-4_4How to use a DOI?

Keywords

Quantum computing; Drug development; Pharmaceutical Research; Molecular modeling; Drug-target interaction; Precision medicine; Lead optimization

Abstract

The journey to develop drugs is long, time-consuming, and costly. Most drugs take years and billions of dollars before they hit the market. Traditional computations remain useful but have inadequacies to accurately reproduce molecular interactions and predict drug efficacy. Quantum computing represents a new tool to potentially revolutionize the challenges brought about by traditional computations in that it can more quickly and accurately solve several of these problems through applying the principles of quantum mechanics. This review examines the possibility of quantum computing to transform pharmaceutical research and drug development. The aim of this review is to give the reader an all-round perspective on the use of quantum computing in drug discovery, with its contributions to molecular modeling, drug-target interaction prediction, and pharmacological property optimization. It promises something new in the application of quantum algorithms to make up for the faults of classical methods used in pharmaceutical research. This review critically synthesizes a number of relevant studies that applied quantum computing capabilities to drug design. Particular attention is brought to quantum algorithms, including but not limited to VQE (Variational Quantum Eigensolver) and QAOA (Quantum Approximate Optimization Algorithm), in determining molecular structures in silico along with the capacity to predict chemical binding affinities. Secondly, the scope of the drugs-discovery application on the various platforms such as IBM Q versus Google Sycamore were compared with current classical computational approach. The quantum computer showed a lot of promise in the simulation of complicated molecular interactions faster than the classical counterparts. A lot of studies emphasize the accuracy quantum simulations have towards the prediction of drug-target binding mechanisms, and this makes the lead optimization process more efficient. However, with scalability and hardware issues, its application in the pharmaceutical research community is still yet to be wide-scale. Quantum computing promises to accelerate the drug development process by improving the speed and accuracy of molecular modeling and drug optimization. Although the field is still in its infancy, its integration into pharmaceutical research could lead to significant reductions in development time and cost. This review underlines the transformative potential of quantum computing and suggests future research directions for overcoming the current limitations and realizing its full potential in precision medicine.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Minakshi Kommoju
AU  - Kanaka Durga Devi Nelluri
AU  - Vijaya Lakshmi Marella
AU  - Bhavana Alaparthi
AU  - Jahnavi Patibandla
AU  - Pamidi Lakshmi Prasanna
AU  - Nalam Vineela
AU  - Nirmala Nirmala
AU  - Padamata Sai Rohith
PY  - 2025
DA  - 2025/08/13
TI  - Accelerating Drug Development: The Role of Quantum Computing in Pharmaceutical Research
BT  - Proceedings of the International symposium on Sustainable Drug Design and Nanoparticle development: Quantum and Computational Perspectives (SDDNDQCP 2025)
PB  - Atlantis Press
SP  - 26
EP  - 41
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-813-4_4
DO  - 10.2991/978-94-6463-813-4_4
ID  - Kommoju2025
ER  -