计算生物学
基质(水族馆)
酶
底物特异性
化学
计算机科学
生物化学
生物
生态学
作者
Leroy P. Nuñez,Álvaro Barrera-Ocampo,Paola A. Caicedo,Natalie Cortés,Edison Osorio,María F. Villegas-Torres,Andrés Fernando González Barrios
出处
期刊:Metabolites
[MDPI AG]
日期:2024-03-07
卷期号:14 (3): 154-154
标识
DOI:10.3390/metabo14030154
摘要
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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