迁移学习改进了酶动力学参数的预测

IF 11.5 Q1 CHEMISTRY, PHYSICAL
Alexander Kroll
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引用次数: 0

摘要

在本期《化学催化》(Chem Catalysis)杂志上,Shen 及其同事介绍了一种用于预测酶动力学参数的新型模型结构和训练技术。提出的模型是朝着开发更精确的动力学预测模型迈出的重要一步,许多重要的工业和科学应用都需要这种模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transfer learning improves predictions of enzyme kinetic parameters

In this issue of Chem Catalysis, Shen and colleagues present a novel model architecture and training technique for predicting enzyme kinetic parameters. The proposed models are an important step toward the development of more accurate kinetic prediction models, which are needed for many important industrial and scientific applications.

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来源期刊
CiteScore
10.50
自引率
6.40%
发文量
0
期刊介绍: Chem Catalysis is a monthly journal that publishes innovative research on fundamental and applied catalysis, providing a platform for researchers across chemistry, chemical engineering, and related fields. It serves as a premier resource for scientists and engineers in academia and industry, covering heterogeneous, homogeneous, and biocatalysis. Emphasizing transformative methods and technologies, the journal aims to advance understanding, introduce novel catalysts, and connect fundamental insights to real-world applications for societal benefit.
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