Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation
The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integration of parameterized quantum circuits into known molecular generative adversarial...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10556803/ |
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| author | Matvei Anoshin Asel Sagingalieva Christopher Mansell Dmitry Zhiganov Vishal Shete Markus Pflitsch Alexey Melnikov |
| author_facet | Matvei Anoshin Asel Sagingalieva Christopher Mansell Dmitry Zhiganov Vishal Shete Markus Pflitsch Alexey Melnikov |
| author_sort | Matvei Anoshin |
| collection | DOAJ |
| description | The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integration of parameterized quantum circuits into known molecular generative adversarial networks and proposes quantum cycle architectures that improve model performance and stability during training. Through extensive experimentation on benchmark drug design datasets, quantum machine 9 (QM9) and PubChemQC 9 (PC9), the introduced models are shown to outperform the previously achieved scores. Most prominently, the new scores indicate an increase of up to 30% in the quantitative estimation of druglikeness. The new hybrid quantum machine learning algorithms, as well as the achieved scores of pharmacokinetic properties, contribute to the development of fast and accurate drug discovery processes. |
| format | Article |
| id | doaj-art-befb5560bf3b49f8b5b0fcb555027d50 |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-befb5560bf3b49f8b5b0fcb555027d502025-01-25T00:03:35ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511410.1109/TQE.2024.341426410556803Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule GenerationMatvei Anoshin0https://orcid.org/0009-0008-5538-4490Asel Sagingalieva1https://orcid.org/0009-0009-3931-3702Christopher Mansell2https://orcid.org/0009-0006-1745-8458Dmitry Zhiganov3https://orcid.org/0009-0004-1376-8366Vishal Shete4https://orcid.org/0009-0003-1556-794XMarkus Pflitsch5Alexey Melnikov6https://orcid.org/0000-0002-5033-4063Terra Quantum AG, St. Gallen, SwitzerlandTerra Quantum AG, St. Gallen, SwitzerlandTerra Quantum AG, St. Gallen, SwitzerlandTerra Quantum AG, St. Gallen, SwitzerlandTerra Quantum AG, St. Gallen, SwitzerlandTerra Quantum AG, St. Gallen, SwitzerlandTerra Quantum AG, St. Gallen, SwitzerlandThe drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integration of parameterized quantum circuits into known molecular generative adversarial networks and proposes quantum cycle architectures that improve model performance and stability during training. Through extensive experimentation on benchmark drug design datasets, quantum machine 9 (QM9) and PubChemQC 9 (PC9), the introduced models are shown to outperform the previously achieved scores. Most prominently, the new scores indicate an increase of up to 30% in the quantitative estimation of druglikeness. The new hybrid quantum machine learning algorithms, as well as the achieved scores of pharmacokinetic properties, contribute to the development of fast and accurate drug discovery processes.https://ieeexplore.ieee.org/document/10556803/Drug designhybrid quantum neural network (HQNN)quantum generative adversarial network (GAN)quantum machine learning (QML)variational quantum circuit (VQC) |
| spellingShingle | Matvei Anoshin Asel Sagingalieva Christopher Mansell Dmitry Zhiganov Vishal Shete Markus Pflitsch Alexey Melnikov Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation IEEE Transactions on Quantum Engineering Drug design hybrid quantum neural network (HQNN) quantum generative adversarial network (GAN) quantum machine learning (QML) variational quantum circuit (VQC) |
| title | Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation |
| title_full | Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation |
| title_fullStr | Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation |
| title_full_unstemmed | Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation |
| title_short | Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation |
| title_sort | hybrid quantum cycle generative adversarial network for small molecule generation |
| topic | Drug design hybrid quantum neural network (HQNN) quantum generative adversarial network (GAN) quantum machine learning (QML) variational quantum circuit (VQC) |
| url | https://ieeexplore.ieee.org/document/10556803/ |
| work_keys_str_mv | AT matveianoshin hybridquantumcyclegenerativeadversarialnetworkforsmallmoleculegeneration AT aselsagingalieva hybridquantumcyclegenerativeadversarialnetworkforsmallmoleculegeneration AT christophermansell hybridquantumcyclegenerativeadversarialnetworkforsmallmoleculegeneration AT dmitryzhiganov hybridquantumcyclegenerativeadversarialnetworkforsmallmoleculegeneration AT vishalshete hybridquantumcyclegenerativeadversarialnetworkforsmallmoleculegeneration AT markuspflitsch hybridquantumcyclegenerativeadversarialnetworkforsmallmoleculegeneration AT alexeymelnikov hybridquantumcyclegenerativeadversarialnetworkforsmallmoleculegeneration |