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.
| Summary: | 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. |
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