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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/14/3/154 |
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| author | Luis F. Salas-Nuñez Alvaro Barrera-Ocampo Paola A. Caicedo Natalie Cortes Edison H. Osorio Maria F. Villegas-Torres Andres F. González Barrios |
| author_facet | Luis F. Salas-Nuñez Alvaro Barrera-Ocampo Paola A. Caicedo Natalie Cortes Edison H. Osorio Maria F. Villegas-Torres Andres F. González Barrios |
| author_sort | Luis F. Salas-Nuñez |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-24T18:01:48Z |
| format | Article |
| id | doaj.art-9d91e700c17642a79708279f54bfd68c |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-04-24T18:01:48Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-9d91e700c17642a79708279f54bfd68c2024-03-27T13:54:09ZengMDPI AGMetabolites2218-19892024-03-0114315410.3390/metabo14030154Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A ReviewLuis F. Salas-Nuñez0Alvaro Barrera-Ocampo1Paola A. Caicedo2Natalie Cortes3Edison H. Osorio4Maria F. Villegas-Torres5Andres F. González Barrios6Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá 111711, ColombiaGrupo Natura, Facultad de Ingeniería, Diseño y Ciencias Aplicadas, Departamento de Ciencias Farmacéuticas y Químicas, Universidad ICESI, Calle 18 No. 122-135, Cali 760031, ColombiaGrupo Natura, Facultad de Ingeniería, Diseño y Ciencias Aplicadas, Departamento de Ciencias Biológicas, Bioprocesos y Biotecnología, Universidad ICESI, Calle 18 No. 122-135, Cali 760031, ColombiaGrupo de Investigación en Química Bioorgánica y Sistemas Moleculares (QBOSMO), Faculty of Natural Sciences and Mathematics, Universidad de Ibagué, Ibagué 730002, ColombiaGrupo de Investigación en Química Bioorgánica y Sistemas Moleculares (QBOSMO), Faculty of Natural Sciences and Mathematics, Universidad de Ibagué, Ibagué 730002, ColombiaCentro de Investigaciones Microbiológicas (CIMIC), Department of Biological Sciences, Universidad de los Andes, Bogotá 111711, ColombiaGrupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá 111711, ColombiaEnzyme–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.https://www.mdpi.com/2218-1989/14/3/154enzyme–substrate interactionartificial intelligencesynthesis routesenzyme classificationmolecular descriptorstraining data |
| spellingShingle | Luis F. Salas-Nuñez Alvaro Barrera-Ocampo Paola A. Caicedo Natalie Cortes Edison H. Osorio Maria F. Villegas-Torres Andres F. González Barrios Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review Metabolites enzyme–substrate interaction artificial intelligence synthesis routes enzyme classification molecular descriptors training data |
| title | 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_short | 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 |
| topic | enzyme–substrate interaction artificial intelligence synthesis routes enzyme classification molecular descriptors training data |
| url | https://www.mdpi.com/2218-1989/14/3/154 |
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