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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48

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多臂强盗算法用于对分子性质的连续实验,具有动态特征选择的算法.

Md Menhazul Abedin1,2, Koji Tabata3,4,5, Yoshihiro Matsumura5

  • 1Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo 060-8628, Japan.

The Journal of chemical physics
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概括

本研究介绍了一种使用强化学习和动态特征选择的新序列优化算法. 它通过适应实验数据,有效地识别最佳分子,优于标准方法.

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科学领域:

  • 计算化学的计算化学
  • 机器学习 机器学习
  • 药物发现 药物发现 药物发现

背景情况:

  • 序列优化旨在找到具有所需属性的最佳候选者,同时尽量减少实验.
  • 特性空间 (例如,分子描述符) 的高维度使得在候选选中使用特征变得复杂.
  • 像贝叶斯优化 (BO) 这样的现有方法经常使用固定的特征集,限制了适应性.

研究的目的:

  • 为分子问题开发一种新的顺序优化算法.
  • 为了应对优化中的高维度和动态特征相关性的挑战.
  • 提高识别最佳分子候选者的效率和可靠性.

主要方法:

  • 开发了一个新的算法,集成强化学习,多臂线性强盗和在线动态特征选择.
  • 实施了一个停止条件,以确保所选候选人的可靠性.
  • 将算法与贝叶斯优化 (BO) 进行比较,使用合成和现实世界的分子数据集 (水合自由能量,反体产物自由能量差异).

主要成果:

  • 开发的算法表现出卓越的性能,特别是在减少找到最佳候选人的时间和停止实验方面.
  • 动态特征选择,通过实验调整分子描述符,显著提高了优化效率.
  • 具有动态特征选择的多臂线性强盗方法优于具有固定特征的标准BO.

结论:

  • 动态特征选择对于高维分子空间中高效的序列优化至关重要.
  • 与传统方法相比,拟议的算法提供了一种更有效和可靠的方法来识别最佳分子候选者.
  • 这项工作推动了机器学习在计算化学和药物发现方面的应用.