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相关实验视频

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A Protocol for Computer-Based Protein Structure and Function Prediction
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TB-IECS:基于机器学习的准确评分功能,用于虚拟查.

Xujun Zhang1, Chao Shen1, Dejun Jiang1

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

Journal of cheminformatics
|July 4, 2023
PubMed
概括
此摘要是机器生成的。

一个新的评分功能,TB-IECS,通过结合能源术语和使用XGBoost来增强虚拟选. 这种方法比药物发现的传统评分功能提高了准确性和效率.

关键词:
机器学习是机器学习.评分功能 评分功能基于理论的相互作用能量组成部分.虚拟选是一个虚拟的选.

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科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 机器学习在药物发现中的作用

背景情况:

  • 基于机器学习的评分函数 (MLSFs) 提供了比经典的评分函数 (SFs) 更好的虚拟选.
  • 在特征生成极限描述器和MLSF中蛋白质-连接体相互作用的特征化中,高计算成本影响了准确性和效率.
  • 开发准确和高效的评分功能对于药物发现中的实际虚拟查至关重要.

研究的目的:

  • 提出一种基于理论的新型相互作用能量成分评分 (TB-IECS),以加强虚拟选.
  • 解决当前MLSF所面临的描述符使用和相互作用表征方面的局限性.
  • 开发一个评分功能,平衡实践应用的效率和准确性.

主要方法:

  • 通过将Smina和NNScore v2的能量术语结合起来,开发了TB-IECS,使用了极端梯度增强 (XGBoost) 算法.
  • 根据公式和物理化学原理,从15个传统的SF中对能量术语进行了分类,产生了324种特征组合.
  • 选择了五个最好的特征组合,用于评估各种特征向量长度,交互类型和ML算法的模型性能.

主要成果:

  • 评估了TB-IECS在DUD-E,LIT-PCBA和七个ChemDiv目标特定数据集上的表现.
  • 证明TB-IECS在虚拟选功率方面表现优于Glide SP和Dock等经典SF.
  • 展示了TB-IECS在实际虚拟查中有效平衡效率和准确性的能力.

结论:

  • TB-IECS在虚拟查的评分功能方面取得了重大进展.
  • 拟议的方法通过优化特征选择和利用强大的ML算法来克服现有的MLSF的局限性.
  • 结核病IECS提供了一个有前途的工具,通过更准确和更有效的虚拟查来加速药物发现.