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相关概念视频

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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相关实验视频

Updated: Jun 22, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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PepExplainer:一种可解释的深度学习模型,用于基于选择的宏环生物活性预测和优化.

Silong Zhai1, Yahong Tan2, Cheng Zhu1

  • 1School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China.

European journal of medicinal chemistry
|June 30, 2024
PubMed
概括
此摘要是机器生成的。

解释性AI模型PepExplainer通过准确预测宏环生物活性来加速药物发现. 它破译了分子结构,克服了当前人工智能方法的局限性,以实现更快的优化.

关键词:
生物活性的预测预测.图形神经网络 (GNN) 是一个神经网络.机器学习 (ML) 是指机器学习.一个宏环类.优化优化 优化优化结构与活动关系 (SAR)

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科学领域:

  • 药物的发现和开发.
  • 计算化学是一种计算化学.
  • 医学中的人工智能

背景情况:

  • 宏环是有希望的药物候选物,但测试成本昂贵.
  • 目前用于生物活性预测的AI模型面临数据限制和解释性问题.

研究的目的:

  • 开发一个可解释的AI模型,PepExplainer,用于准确的宏环生物活性预测.
  • 解决AI驱动药物发现中的有限数据和模型可解释性的挑战.

主要方法:

  • 开发了PepExplainer,这是一个使用子结构面具解释 (SME) 的图形神经网络.
  • 将宏环转化为原子级分子图,处理复杂结构和非正规氨基酸.
  • 利用丰度和生物活性数据与转移学习进行改进的预测.

主要成果:

  • PepExplainer在预测宏环生物活性方面表现出有效性,在新合成的上得到了验证.
  • 该模型成功地将宏环的IC50从15nM优化到5.6nM.
  • PepExplainer确定了促进生物活性的关键分子模式.

结论:

  • PepExplainer通过提供对宏环的可解释预测来增强人工智能驱动的药物发现.
  • 该模型可以加速识别和优化基于的新型治疗方法.
  • 可解释的人工智能提供了一种强大的方法来克服传统药物开发管道的局限性.