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Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery by Maria Avramouli; Ilias K. Savvas; Anna Vasilaki; Georgia Garani is a Engineering article available to read on EtoBox.

What is Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery about?

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of machine learning (ML), leading to the emergence of quantum machine learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong

Who reads Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery?

Author

Maria Avramouli; Ilias K. Savvas; Anna Vasilaki; Georgia Garani

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)