Quantum Machine Learning for Drug Discovery: A Systematic Review
DOI:
https://doi.org/10.63503/j.ijcma.2025.156Keywords:
Quantum Machine Learning, Drug Discovery, Molecular Simulation, Molecule Generation, Noisy intermediate-scale Quantum (NISQ)Abstract
Quantum computing plays a significant role in simulating molecules and atoms and offers advantages in chemistry over classical computing. The potential of Quantum Machine Learning (QML) can be used in drug discovery, chemical reaction simulations, and Material design for pharmaceuticals. QML leverages quantum computing and advanced machine learning to accelerate the identification of drug candidates, predict molecular interactions, and optimize compounds. In this paper, we present a systematic review of the methods used for molecular property prediction and molecular generation using quantum machine learning. We have included the recent research, perspective, advantages, and challenges that must be addressed to achieve this task. The objective of this research is not only to discuss current strategies and methods used for drug discovery but also to promote interdisciplinary research in the field of quantum computing and chemistry for wellness.
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