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相关概念视频

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

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全面的,开源和自动化工作流程,用于多站点的优化中的λ-动态.

Renling Hu1,2,3, Jintu Zhang1, Yu Kang1

  • 1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

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|February 1, 2024
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概括

针对多站式λ-动力学 (MSLD) 的新自动化工作流程使具有约束力的自由能量计算更易于用于药物发现引优化. 与以前的方法相比,这种方法显著提高了计算效率和准确性.

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科学领域:

  • 计算化学和分子建模.
  • 药物发现和优化.
  • 生物物理和分子动力学模拟.

背景情况:

  • 多站点 λ-动力学 (MSLD) 是一种高效的结合性自由能计算方法,具有潜在的优化.
  • 目前的MSLD方法由于复杂的数据准备和模拟过程而面临挑战,这限制了它们的广泛应用.
  • 需要可访问和自动化工作流程,以促进MSLD计算在各种蛋白质 - 连接体系统.

研究的目的:

  • 为MSLD计算开发一个全面的,开源的,自动化的工作流程.
  • 提高MSLD的准确性和效率,以优化药物发现中的领先优化.
  • 为了使MSLD计算更容易获得研究人员没有专门的专业知识.

主要方法:

  • 基于BLaDE动态引擎的自动化工作流程的开发.
  • 整合了基于Ligand内部和笛卡尔坐标重建的对齐算法 (LIC-align) 和一个优化的最大共同子结构 (MCS) 搜索算法.
  • 通过计算具有显著结构变异的大规模同源配体的相对结合自由能量来验证.

主要成果:

  • 计算和实验结合的自由能量之间有很好的一致性 (平均无标记误差为1.08 ± 0.47 kcal/mol).
  • 证明了高精度, >57.1%的连接体显示误差<1.0 kcal/mol和皮尔森相关系数为0.88.8.
  • 与传统的自由能量扰动 (FEP) 方法相比,计算效率达到一个数量级的速度,准确度可比.

结论:

  • 开发的自动化MSLD工作流程是准确的,高效的,和可访问的,显著推进其在药物发现优化应用.
  • 该工作流成功地处理复杂的分子系统和具有挑战性的子结构,优于以前的方法.
  • 通过这种新的工作流来推动的MSLD,成为指导药物发现工作的有竞争力和有价值的工具.