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相关概念视频

Diffusion01:12

Diffusion

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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相关实验视频

Updated: Sep 16, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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BoKDiff:用于特定目标的3D分子生成的最佳K扩散对齐.

Ali Khodabandeh Yalabadi1, Mehdi Yazdani-Jahromi2, Ozlem Ozmen Garibay1

  • 1Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, United States.

Bioinformatics advances
|July 7, 2025
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概括
此摘要是机器生成的。

通过整合最佳的K对齐和最佳的N采样,BoKDiff增强了基于结构的药物设计的配体生成. 这种新的方法提高了分子质量和生成率,推动了治疗的发展.

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科学领域:

  • 计算化学和化学信息学
  • 人工智能在药物发现中的作用
  • 分子建模和模拟分子模型

背景情况:

  • 基于结构的药物设计 (SBDD) 依赖于目标蛋白3D结构来开发药物.
  • 生成模型显示出潜力,但在有限的数据和调整问题方面面临挑战.
  • 现有的方法在有效的蛋白质-连接体复合体生成方面扎.

研究的目的:

  • 引入BoKDiff,这是SBDD中增强连接体生成的新框架.
  • 解决当前生成模型中数据稀缺性和对齐精度的局限性.
  • 提高产生的候选药物的质量和多样性.

主要方法:

  • BoKDiff框架将多目标优化与最佳K对齐结合起来.
  • 连接体质量中心重新定位以匹配对接姿势,以准确地提取子组件.
  • 最好的N (BoN) 采样策略用于在没有微调的情况下进行最佳候选人选择.
  • 使用DecompDiff模型作为配体生成的基础.

主要成果:

  • 在药物相似性 (QED>0.6) 和合成可访问性 (SA>0.75) 方面,BoKDiff获得了高分.
  • 最好的N抽样策略在候选人选择中显示出超过35%的成功率.
  • 在CrossDocked2020数据集中,BoKDiff的表现优于之前的模型,平均对接得分为-8.58.
  • 实现了26%的有效分子生成率,大大改善了现有方法.

结论:

  • BoKDiff代表了基于结构的药物设计联体生成的重大进步.
  • 最好的K对齐和BoN采样的整合提供了一个强大的新策略.
  • 这种方法有可能实现实用,高质量的候选药物设计和开发.