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<p>Enzyme classification with machine learning (ML) and deep learning (DL) is a novel approach in bioinformatics that significantly enhances the prediction of enzyme functions and properties. Phylogenetic analysis is often incorporated into traditional enzyme classification methods; however, the availability of homologous sequences can limit the applications of these methods. On the other hand, ML and DL techniques use large datasets to enhance classification precision and reveal new roles for enzymes, thus resolving issues associated with unidentified enzyme characteristics. Machine learning and deep learning algorithms have demonstrated superior performance in enzyme classification tasks, particularly when utilizing convolutional and recurrent neural networks. Compared to conventional methods, these advanced techniques can enhance predictive skills by recognizing complex patterns within amino acid sequences. The continued integration of computational methods into enzyme engineering holds promise for accelerating the search for new enzymes and optimizing their properties for various applications. Furthermore, the development of reliable predictive models will be crucial for creating practical bioinformatics tools and enzyme categorization systems in the future. Sufficient research in this field is necessary to completely utilize ML and DL's enzyme characterization capabilities in contrast to conventional techniques. This study thoroughly examines existing research on ML and DL applications in enzyme class prediction, seeking to identify areas for improvement and inform future research directions.</p>

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Exploration of Computational Approaches in Enzyme Classification: Problems and Recent Development—A Critical Review

  • 期刊论文

  • 发表日期：

    2025年9月5日

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  • Prabha Singh,
  • Sudhakar Tripathi,
  • Anand Bihari

摘要

Enzyme classification with machine learning (ML) and deep learning (DL) is a novel approach in bioinformatics that significantly enhances the prediction of enzyme functions and properties. Phylogenetic analysis is often incorporated into traditional enzyme classification methods; however, the availability of homologous sequences can limit the applications of these methods. On the other hand, ML and DL techniques use large datasets to enhance classification precision and reveal new roles for enzymes, thus resolving issues associated with unidentified enzyme characteristics. Machine learning and deep learning algorithms have demonstrated superior performance in enzyme classification tasks, particularly when utilizing convolutional and recurrent neural networks. Compared to conventional methods, these advanced techniques can enhance predictive skills by recognizing complex patterns within amino acid sequences. The continued integration of computational methods into enzyme engineering holds promise for accelerating the search for new enzymes and optimizing their properties for various applications. Furthermore, the development of reliable predictive models will be crucial for creating practical bioinformatics tools and enzyme categorization systems in the future. Sufficient research in this field is necessary to completely utilize ML and DL's enzyme characterization capabilities in contrast to conventional techniques. This study thoroughly examines existing research on ML and DL applications in enzyme class prediction, seeking to identify areas for improvement and inform future research directions.