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相关实验视频

Updated: Jul 17, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

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

Published on: June 20, 2025

322

机器学习增强的对接使得基于结构的高效虚拟选能够实现千兆级数量的化学库.

Toni Sivula1, Laxman Yetukuri2, Tuomo Kalliokoski3

  • 1School of Pharmacy, University of Eastern Finland, Kuopio FI-70211, Finland.

Journal of chemical information and modeling
|September 1, 2023
PubMed
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像HASTEN这样的机器学习 (ML) 策略通过快速选数十亿种化合物来加速药物发现. 哈斯顿通过对接仅1%的图书馆,实现了90%的顶级热门回忆,大大减少了选时间.

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 机器学习是机器学习.

背景情况:

  • 超大的选库对传统的基于对接的虚拟选构成了挑战.
  • 现有的方法与千兆级复合库的规模扎.

研究的目的:

  • 评估用于加速千兆级图书馆虚拟选的HASTEN工具.
  • 评估HASTEN在召回抗菌和抗病毒点的最高得分化合物的效率.

主要方法:

  • 通过使用Glide高通量虚拟选协议,为156亿个化合物生成了一种蛮力对接基线.
  • 应用了HASTEN,一种机器学习增强的策略,选了图书馆的一小部分 (1%).
  • 研究了结约束对对接和ML预测的影响.

主要成果:

  • 哈斯通过对抗抗菌和抗病毒目标的图书馆仅 1% 的对接,实现了 90% 的顶级 1000 个虚拟点击的回忆.
  • 将所需的对接实验减少了99%,大大缩短了选时间.
  • 在处理ML增强选的失败对接尝试时识别了优化潜力.

结论:

  • 哈斯顿是一个快速而强大的工具,用于选药物发现中的千兆级图书馆.

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  • 机器学习增强的策略在吞吐量和效率方面比武力对接提供了显著的优势.
  • 哈斯顿为药物发现活动提供了巨大的化学空间.