Duong, Hoang Phi Yen, McNiven, Brad D. E., Dobre, Octavia A., Rizvi, Syed Muhammad Abuzar, Shin, Hyundong, Nguyen, Tuan Thanh ORCID: https://orcid.org/0000-0003-0055-8218 and Duong, Trung Q. (2025) Quantum machine learning for drug discovery: taxonomy, research challenges, and the road ahead. ACM Computing Surveys (CSUR). ISSN 0360-0300 (Print), 1557-7341 (Online) (In Press)

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Abstract

The recent pandemic outbreak has posed significant challenges for medical research, particularly in drug discovery. Machine learning (ML) has become increasingly prevalent in various stages of drug discovery, aiming to support the advancement of new drug research while reducing time and cost investments. Furthermore, the emergence of quantum computing and quantum machine learning (QML) represents a significant advancement in this field, offering the ability to tackle the complex processes involved in drug discovery. This review provides a comprehensive perspective, comparing advanced QML to classical ML in drug discovery applications including drug design, virtual screening, and ADMET (absorption, distribution, metabolism, excretion) and toxicity prediction. Additionally, we summarize the current applications of QML algorithms to real-world data sets utilized in clinical research and drug discovery.

Item Type:	Article
Uncontrolled Keywords:	Quantum Machine Learning, drug discovery and development, clinical research
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	Organisation
Last Modified:	04 Dec 2025 12:00
URI:	https://gala.gre.ac.uk/id/eprint/51853

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