Quantum Machine Learning in Drug Discovery: Current State and Challenges

Avramouli, Maria; Savvas, Ilias Κ.; Vasilaki, Anna; Garani, Georgia; Xenakis, Apostolos

doi:10.1145/3575879.3576024

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Electronics 2023, 12, x FOR PEER REVIEW 3 of 28 in [23,24] in chemistry. Finally, [25] was the first review that explored QML models applied to the drug discovery pipeline. In this review, the evolution of the use of QML algorithms in drug discovery is examined over the past five years.…”

Section: Drug Discoverymentioning

confidence: 99%

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Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

Avramouli¹,

Savvas²,

Vasilaki³

et al. 2023

Preprint

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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 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 rivals against the classical ones and a hybrid solution is the recommended approach at present.

show abstract

Section: Drug Discoverymentioning

confidence: 99%

“…The review in [22] presented QC algorithms utilized in computational biology and in [23,24] in chemistry. Finally, [25] was the first review that explored QML models applied to the drug discovery pipeline.…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

Avramouli¹,

Savvas²,

Vasilaki³

et al. 2023

Preprint

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A brief review on quantum computing based drug design

Das,

Ray,

Bhattacharyya

et al. 2024

WIREs Data Min & Knowl

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Design and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug molecule to its commercialization in the market. One of the most critical steps in drug design is to find the molecular interactions between the target (infected) molecule and the drug molecule. Several complex chemical equations need to be solved to determine the molecular interactions. In the late 20th Century, the advancement of computational technologies has made the solution of chemical equations relatively easier and faster. Moreover, the design of drug molecules involves multi‐criteria optimization. Classical computational methodologies have been used for drug design since the end of the 20th Century. However, nowadays, more advanced computational methodologies are inevitable in designing drugs for new diseases and drugs with fewer side effects. In this context, the quantum computing paradigm has proved beneficial in drug design due to its advanced computational capabilities. This paper presents a state‐of‐the‐art comprehensive review of the quantum computing‐based methodologies involved in drug design. A comparative study is made about the different quantum‐aided drug design methods, stating each methodology's merits and demerits. The review work presented in this manuscript will help new researchers assess the present state‐of‐the‐art concept of quantum‐based drug design.This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Computational Intelligence Application Areas > Health Care

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Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

et al. 2023

Self Cite

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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 rivals to the classical ones, and a hybrid solution is the recommended approach at present.

show abstract

Quantum Machine Learning in Drug Discovery: Current State and Challenges

Cited by 3 publications

References 32 publications

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

A brief review on quantum computing based drug design

Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

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