Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review
Enzyme–substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addresse...
- Autores:
-
Salas-Nuñez, Luis F.
Barrera-Ocampo, Alvaro
Caicedo, Paola A.
Cortes, Natalie
Osorio, Edison H
Villegas-Torres, Maria F.
González Barrios, Andres F.
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/5807
- Palabra clave:
- Aprendizaje automático
Artificial intelligence
Computational studies
Enzyme classification
Enzyme–substrate interaction
Molecular descriptors
Synthesis routes
Rraining data
- Rights
- openAccess
- License
- © 2024 by the authors.
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| dc.title.eng.fl_str_mv |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review |
| title |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review |
| spellingShingle |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review Aprendizaje automático Artificial intelligence Computational studies Enzyme classification Enzyme–substrate interaction Molecular descriptors Synthesis routes Rraining data |
| title_short |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review |
| title_full |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review |
| title_fullStr |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review |
| title_full_unstemmed |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review |
| title_sort |
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review |
| dc.creator.fl_str_mv |
Salas-Nuñez, Luis F. Barrera-Ocampo, Alvaro Caicedo, Paola A. Cortes, Natalie Osorio, Edison H Villegas-Torres, Maria F. González Barrios, Andres F. |
| dc.contributor.author.none.fl_str_mv |
Salas-Nuñez, Luis F. Barrera-Ocampo, Alvaro Caicedo, Paola A. Cortes, Natalie Osorio, Edison H Villegas-Torres, Maria F. González Barrios, Andres F. |
| dc.subject.armarc.none.fl_str_mv |
Aprendizaje automático |
| topic |
Aprendizaje automático Artificial intelligence Computational studies Enzyme classification Enzyme–substrate interaction Molecular descriptors Synthesis routes Rraining data |
| dc.subject.proposal.eng.fl_str_mv |
Artificial intelligence Computational studies Enzyme classification Enzyme–substrate interaction Molecular descriptors Synthesis routes Rraining data |
| description |
Enzyme–substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives. |
| publishDate |
2024 |
| dc.date.issued.none.fl_str_mv |
2024-03 |
| dc.date.accessioned.none.fl_str_mv |
2025-10-17T23:24:53Z |
| dc.date.available.none.fl_str_mv |
2025-10-17T23:24:53Z |
| dc.type.none.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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Salas-Nuñez, L., Barrera-Ocampo, A., Caicedo, P., Cortes, N., Osorio, E., Villegas-Torres, M. y González Barrios, A. (2024). Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review. Metabolites, 14(3). DOI: 10.3390/metabo14030154 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/metabo14030154 |
| dc.identifier.issn.none.fl_str_mv |
22181989 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12313/5807 |
| dc.identifier.url.none.fl_str_mv |
https://www.mdpi.com/2218-1989/14/3/154 |
| identifier_str_mv |
Salas-Nuñez, L., Barrera-Ocampo, A., Caicedo, P., Cortes, N., Osorio, E., Villegas-Torres, M. y González Barrios, A. (2024). Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review. Metabolites, 14(3). DOI: 10.3390/metabo14030154 10.3390/metabo14030154 22181989 |
| url |
https://hdl.handle.net/20.500.12313/5807 https://www.mdpi.com/2218-1989/14/3/154 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.citationissue.none.fl_str_mv |
3 |
| dc.relation.citationvolume.none.fl_str_mv |
14 |
| dc.relation.ispartofjournal.none.fl_str_mv |
Metabolites |
| dc.relation.references.none.fl_str_mv |
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Salas-Nuñez, Luis F.13ef3bff-84c8-4225-821a-36450b94a55a-1Barrera-Ocampo, Alvaro523fb227-3b4d-4781-9e26-3de0f8c13cea-1Caicedo, Paola A.2ab1bc0c-d668-4bd3-90c6-664c5bda174f-1Cortes, Nataliece9c654f-72b9-4163-a73a-c1c71903ba7e-1Osorio, Edison H087e0c0b-d49f-4915-b7fa-272d785c30af-1Villegas-Torres, Maria F.cc6dce60-c296-4a92-9343-bc93bd09ecb1-1González Barrios, Andres F.12232898-90c2-4399-9193-98b116a31116-12025-10-17T23:24:53Z2025-10-17T23:24:53Z2024-03Enzyme–substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives.application/pdfSalas-Nuñez, L., Barrera-Ocampo, A., Caicedo, P., Cortes, N., Osorio, E., Villegas-Torres, M. y González Barrios, A. (2024). Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review. Metabolites, 14(3). 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In Advances in Artificial Intelligence, Proceedings of the AI 2006: Advances in Artificial Intelligence, Hobart, Australia, 4–8 December 2006; Sattar, A., Kang, B., Eds.; Springer: Berlin/Heidelberg, Germany, 2006; pp. 1015–1021.© 2024 by the authors.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/Aprendizaje automáticoArtificial intelligenceComputational studiesEnzyme classificationEnzyme–substrate interactionMolecular descriptorsSynthesis routesRraining dataMachine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A ReviewArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/ee750d3e-446f-4fe1-b2aa-b51b4fc3c7ed/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51TEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain2085https://repositorio.unibague.edu.co/bitstreams/d81e1b1c-c40c-4601-b73d-4cc8ba7ae61a/download26534add8b0bd66790d3b18e52e267c1MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg21390https://repositorio.unibague.edu.co/bitstreams/c2c85e43-af90-4ea3-af5c-3546b4e08f05/downloadb5624d39030e67e1e36de46d45e8d657MD54ORIGINALArtículo.pdfArtículo.pdfapplication/pdf86456https://repositorio.unibague.edu.co/bitstreams/e1bd6601-0db3-49b6-8f18-87ca1b28e77f/download82d4b66a9f732e7d21ea0942525efc90MD5220.500.12313/5807oai:repositorio.unibague.edu.co:20.500.12313/58072025-10-18 03:02:51.831https://creativecommons.org/licenses/by-nc/4.0/© 2024 by the authors.https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8= |