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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.6K

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相关实验视频

Updated: May 24, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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GNINA 1.3:分子对接与深度学习的下一个增量.

Andrew T McNutt1, Yanjing Li2, Rocco Meli3,4

  • 1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.

Journal of cheminformatics
|March 2, 2025
PubMed
概括

开源分子对接软件GNINA已更新到1.3版本. 这一版本提高了计算效率,并为药物设计引入了共价对接功能.

关键词:
深度学习是一种深度学习.分子对接是分子对接.基于结构的药物设计.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 分子对接对于基于结构的药物设计至关重要.
  • 计算机辅助药物设计 (CADD) 旨在降低药物开发成本.
  • GNINA是一个开源的分子对接软件.

研究的目的:

  • 为了介绍GNINA分子对接软件的1.3版本.
  • 为了提高计算效率和扩大对接能力.
  • 为了促进高通量虚拟选.

主要方法:

  • 更新了PyTorch的深度学习框架,以提高计算效率.
  • 在CrossDocked2020 v1.3数据集上重新训练的卷积神经网络 (CNN) 评分功能.
  • 引入了知识蒸的CNN评分功能和共价对接功能.

主要成果:

  • 通过PyTorch集成实现了更高效的计算对接.
  • 启用了高通量虚拟选与新的CNN评分功能.
  • 扩大了GNINA的范围,添加了共价对接.

结论:

  • GNINA 1.3 提供了对共价对接和改进的深度学习模型的增强支持.
  • 更新后的软件有助于更有效的分子对接和虚拟选.
  • GNINA继续成为一种用户友好的,开源的药物发现框架.