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Updated: Jan 29, 2026

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使用iESCC准确的酶特异性常数预测

Yu Zhang1, Li-Hua Liu2, Shuqi Wang2

  • 1Biosciences Central Research Facility, The Hong Kong University of Science and Technology, GuangZhou 510000, China.

Bioresource technology
|January 27, 2026
PubMed
概括

一个新的深度学习模型,iESC,从酶序列和基质结构准确地预测了诸如迈凯利斯常数 (Km) 和循环数 (kcat) 等酶动态参数,加速了酶研究.

关键词:
深度学习是一种深度学习.深度突变扫描 进行深度突变扫描.酶特异性常数 酶特异性常数功能提取 功能提取高通量选的高通量选

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