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相关概念视频

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The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...
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Updated: Jun 5, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
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杜里安:基于结构的3D分子生成的全面基准.

Dou Nie1, Huifeng Zhao1, Odin Zhang1

  • 1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China.

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|December 16, 2024
PubMed
概括

杜兰,一个新的框架,使用多种指标评估基于结构的3D分子生成模型. 它揭示了药物发现中新性和实用性的平衡的局限性,强调了多目标优化.

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科学领域:

  • 计算化学计算化学
  • 药物发现 药物发现 药物发现
  • 化学中的人工智能.

背景情况:

  • 深度神经网络用于3D分子生成,利用目标结构信息.
  • 这些模型的当前评估方法受到数据偏差和单个指标的限制.
  • 由于评估标准不一致,比较生成模型具有挑战性.

研究的目的:

  • 介绍Durian,一个基于结构的3D分子生成的全面评估框架.
  • 使用多度指标方法评估六种领先的3D分子生成方法.
  • 为药物设计生成模型的选择和优化提供指导.

主要方法:

  • 开发了Durian,结合了蛋白质-配体数据,实验亲和力,以及物理化学/几何指标.
  • 在"Dock"和"Score"模式中使用了三种对接方法 (QuickVina2,Surflex,Gnina) 来进行绑定亲和度评估.
  • 应用Durian对六个3D分子生成模型进行了基准测试:LiGAN,Pocket2Mol,DiffSBDD,SBDD,GraphBP和SurfGen.

主要成果:

  • 大多数模型都产生了具有合理性质的药物样分子,但在新性,理性和合成可访问性方面遇到了困难.
  • 杜里安揭示了各种模型的不同局限性,强调了需要多目标优化.
  • SurfGen和SBDD的整体表现强,但需要在构造合理性方面进行改进.

结论:

  • 杜里安为评估3D分子生成模型提供了一个强大的框架.
  • 多目标优化对于开发用于药物发现的实用3D生成模型至关重要.
  • 该框架为药物设计管道中选择和完善生成模型提供了指导.