Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery
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 r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/11/2402 |
| _version_ | 1797597758694096896 |
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| author | Maria Avramouli Ilias K. Savvas Anna Vasilaki Georgia Garani |
| author_facet | Maria Avramouli Ilias K. Savvas Anna Vasilaki Georgia Garani |
| author_sort | Maria Avramouli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:10:01Z |
| format | Article |
| id | doaj.art-da42052b61be4367bcf7950dc8d3f80e |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:10:01Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-da42052b61be4367bcf7950dc8d3f80e2023-11-18T07:44:26ZengMDPI AGElectronics2079-92922023-05-011211240210.3390/electronics12112402Unlocking the Potential of Quantum Machine Learning to Advance Drug DiscoveryMaria Avramouli0Ilias K. Savvas1Anna Vasilaki2Georgia Garani3Department of Digital Systems, University of Thessaly, 41500 Larissa, GreeceDepartment of Digital Systems, University of Thessaly, 41500 Larissa, GreeceLab of Pharmacology, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41221 Larissa, GreeceDepartment of Digital Systems, University of Thessaly, 41500 Larissa, GreeceThe 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.https://www.mdpi.com/2079-9292/12/11/2402drug discoverydrug designdrug developmentquantum computingquantum machine learning |
| spellingShingle | Maria Avramouli Ilias K. Savvas Anna Vasilaki Georgia Garani Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery Electronics drug discovery drug design drug development quantum computing quantum machine learning |
| title | Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery |
| title_full | Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery |
| title_fullStr | Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery |
| title_full_unstemmed | Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery |
| title_short | Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery |
| title_sort | unlocking the potential of quantum machine learning to advance drug discovery |
| topic | drug discovery drug design drug development quantum computing quantum machine learning |
| url | https://www.mdpi.com/2079-9292/12/11/2402 |
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