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相关概念视频

The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Sep 10, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

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机器学习潜力的基本稳定性测试用于计算药物发现中的分子模拟

Kavindri Ranasinghe1,2, Adam L Baskerville2, Geoffrey P F Wood1,2

  • 1Recursion, Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.

Journal of chemical information and modeling
|August 27, 2025
PubMed
概括

神经网络潜能提供快速的分子模拟, 但需要仔细的测试. 这项研究评估了各种模型,发现稳定性和准确性的显著差异,强调了在应用中需要严格的选择.

科学领域:

  • 计算化学
  • 材料科学
  • 机器学习

背景情况:

  • 在量子力学数据上训练的神经网络潜能 (NNP) 提供了高效的分子相互作用计算.
  • 然而,NNP可以表现出不稳定性,非物理行为或不充分的准确性,限制它们在分子模拟中的使用.

研究的目的:

  • 为分子模拟系统地评估各种神经网络潜力的可靠性和性能.
  • 使用一致的测试框架评估模型架构对NNP性能的影响.

主要方法:

  • 在八个内部NNP (ANI-2x和MACE架构) 和四个公共NNP (ANI-2x,ANI-1ccx,MACE-OFF23,AIMNet2) 上进行了气体和凝结相稳定性测试.
  • 对基准分子和分子动力学模拟进行正常模式分析,以确定不稳定性和非物理行为.
  • 评估了蛋白质 - 连接体相互作用能量,并将结果与实验结合亲和度和其他计算方法进行了比较.

主要成果:

  • 观察到模型性能的显著变化;一些MACE模型在模拟和硬体碰撞期间显示不稳定性.
  • 发表的ANI-2x和一个内部MACE模型无法准确地表示液态水,形成固态.
  • ANI-1ccx在凝结水中表现出非物理能量最小值,导致相变.
  • 一个内部NNP与标准模型相比,与实验水结构有更好的一致性.

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  • 大多数NNP与实验结合亲缘关系的相关性比对接得分更好,ANI-2x接近DFT的准确性.
  • 结论:

    • 没有一个NNP是普遍适用的;模型架构和训练数据显著影响性能.
    • 在不同阶段和条件下进行严格的测试对于选择可靠的NNP至关重要.
    • 在NNP培训和选择过程中仔细考虑是成功的现实应用的关键.