Quantum algorithm for bioinformatics to compute the similarity between proteins
Abstract Drug discovery has become a main challenge in the society, following the COVID‐19 pandemic. However, pharmaceutical companies are already using computing to accelerate drug discovery and are increasingly interested in quantum computing (QC), with a view to improving the speed of development...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | IET Quantum Communication |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/qtc2.12098 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846100864962396160 |
|---|---|
| author | Anthony Chagneau Yousra Massaoudi Imene Derbali Linda Yahiaoui |
| author_facet | Anthony Chagneau Yousra Massaoudi Imene Derbali Linda Yahiaoui |
| author_sort | Anthony Chagneau |
| collection | DOAJ |
| description | Abstract Drug discovery has become a main challenge in the society, following the COVID‐19 pandemic. However, pharmaceutical companies are already using computing to accelerate drug discovery and are increasingly interested in quantum computing (QC), with a view to improving the speed of development process for new drugs. The authors propose a quantum method for generating random sequences based on occurrence in a protein database and quantum algorithms for calculating a similarity rate between proteins. Both concepts can be used for structure prediction in drug design. The aim is to find the proteins closest to the generated protein and obtain an ordering of these proteins. First, the authors will present the construction of a quantum protein generator that defines a protein, called a test protein. The authors will then describe different methods to compute the similarity's rate between each protein in the database and the test protein or, for a case study, the elafin. The algorithms have been extended or adapted to a quantum formalism for use cases, that is, amino acid sequences, and tested to see the added value of quantum versions. The interest is to observe whether QC can be used in the drug discovery process. |
| format | Article |
| id | doaj-art-e5f700c4a9c34aceb5001c62e9f6b9c0 |
| institution | Kabale University |
| issn | 2632-8925 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Quantum Communication |
| spelling | doaj-art-e5f700c4a9c34aceb5001c62e9f6b9c02024-12-29T13:34:29ZengWileyIET Quantum Communication2632-89252024-12-015441744210.1049/qtc2.12098Quantum algorithm for bioinformatics to compute the similarity between proteinsAnthony Chagneau0Yousra Massaoudi1Imene Derbali2Linda Yahiaoui3Expleo Group Agence Méditerranée Vitrolles FranceExpleo Group Saint‐Priest FranceExpleo Group Saint‐Priest FranceExpleo Group Saint‐Priest FranceAbstract Drug discovery has become a main challenge in the society, following the COVID‐19 pandemic. However, pharmaceutical companies are already using computing to accelerate drug discovery and are increasingly interested in quantum computing (QC), with a view to improving the speed of development process for new drugs. The authors propose a quantum method for generating random sequences based on occurrence in a protein database and quantum algorithms for calculating a similarity rate between proteins. Both concepts can be used for structure prediction in drug design. The aim is to find the proteins closest to the generated protein and obtain an ordering of these proteins. First, the authors will present the construction of a quantum protein generator that defines a protein, called a test protein. The authors will then describe different methods to compute the similarity's rate between each protein in the database and the test protein or, for a case study, the elafin. The algorithms have been extended or adapted to a quantum formalism for use cases, that is, amino acid sequences, and tested to see the added value of quantum versions. The interest is to observe whether QC can be used in the drug discovery process.https://doi.org/10.1049/qtc2.12098graph theoryprobabilityquantum computingrandom processes |
| spellingShingle | Anthony Chagneau Yousra Massaoudi Imene Derbali Linda Yahiaoui Quantum algorithm for bioinformatics to compute the similarity between proteins IET Quantum Communication graph theory probability quantum computing random processes |
| title | Quantum algorithm for bioinformatics to compute the similarity between proteins |
| title_full | Quantum algorithm for bioinformatics to compute the similarity between proteins |
| title_fullStr | Quantum algorithm for bioinformatics to compute the similarity between proteins |
| title_full_unstemmed | Quantum algorithm for bioinformatics to compute the similarity between proteins |
| title_short | Quantum algorithm for bioinformatics to compute the similarity between proteins |
| title_sort | quantum algorithm for bioinformatics to compute the similarity between proteins |
| topic | graph theory probability quantum computing random processes |
| url | https://doi.org/10.1049/qtc2.12098 |
| work_keys_str_mv | AT anthonychagneau quantumalgorithmforbioinformaticstocomputethesimilaritybetweenproteins AT yousramassaoudi quantumalgorithmforbioinformaticstocomputethesimilaritybetweenproteins AT imenederbali quantumalgorithmforbioinformaticstocomputethesimilaritybetweenproteins AT lindayahiaoui quantumalgorithmforbioinformaticstocomputethesimilaritybetweenproteins |