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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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PROTAC-Splitter:用于自动识别 PROTAC 子结构的机器学习框架.

Stefano Ribes1, Ranxuan Zhang1, Télio Cropsal1

  • 1Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Chalmersplatsen 1, 412 96, Gothenburg, Sweden.

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|February 20, 2026
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概括
此摘要是机器生成的。

机器学习工具PROTAC-Splitter自动化了针对蛋白质溶解的嵌合体 (PROTAC) 组件的注释. 它使用混合方法可靠地分析各种PROTAC结构,克服数据稀缺的挑战.

关键词:
化学信息学 化学信息学药物发现 药物发现机器学习是机器学习.这就是PROTAC.有针对性的蛋白质降解降解.

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科学领域:

  • 药用化学 医学化学
  • 计算化学计算化学
  • 药物发现 药物发现 药物发现

背景情况:

  • 化向的仿真体 (PROTACs) 是具有治疗潜力的异构生物功能分子.
  • 手动注释 PROTAC 组件 (E3 合酶连接体,链接器,弹头) 是具有挑战性和耗时的.
  • 需要自动化方法来准确识别和注释 PROTAC 的子结构.

研究的目的:

  • 开发和验证PROTAC-Splitter,这是一个用于自动化PROTAC子结构注释的机器学习框架.
  • 通过生成和发布注释的PROTACs的大型合成数据集来解决数据稀缺问题.
  • 为了比较不同的机器学习模型用于PROTAC注释.

主要方法:

  • 开发PROTAC-Splitter,一个机器学习框架.
  • 创建一个由130万条注释的PROTAC结构组成的合成数据集.
  • 实现基于变压器和基于XGBoost的模型用于亚结构注释.
  • 对公共和专有 PROTAC 数据集的评估,包括结构新型化合物.
  • 开发一个变压器封装 (变压器-Δ) 来纠正预测错误.

主要成果:

  • 变压器模型在公共数据上实现了86%的精确匹配准确度,但在新型结构方面遇到了困难.
  • XGBoost模型确保了化学有效性和完美的重新组装,但具有较低的准确匹配精度.
  • 变压器-Δ将重组精度提高到96% (公共) 和70% (内部数据集).
  • 结合变压器-Δ和XGBoost的混合方法在各种化学空间中展示了强大的注释.

结论:

  • PROTAC-Splitter提供了一种可靠且可扩展的解决方案,用于自动化的 PROTAC 分析.
  • 混合方法有效地注释了PROTAC,克服了单个模型的局限性.
  • 由于PROTAC-Splitter的开源可用性,使其在药物发现中得到更广泛的采用.