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Updated: Jun 12, 2025

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基于内存内核最小化的神经网络,用于发现生物分子动态的缓慢集体变量

Bojun Liu1, Siqin Cao1, Jordan G Boysen1

  • 1Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, USA.

Nature computational science
|June 10, 2025
PubMed
概括

本研究介绍了MEMnets,这是一种用于识别关键蛋白质运动的深度学习方法. MEMnets准确地捕捉了生物分子系统中的缓慢动态,有助于理解复杂的生物过程.

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Nuclear relaxation restores the equilibrium population imbalance and can occur via spin–lattice or spin–spin mechanisms, which are first-order exponential decay processes.
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers...
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