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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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MDTips:一个基于多式联络数据的药物向相互作用预测系统,融合了知识,基因表达特征和结构数据.

Xiaoqiong Xia1, Chaoyu Zhu2, Fan Zhong2

  • 1Institutes of Biomedical Sciences, Fudan University, No. 131, Dong An Road, Shanghai, Shanghai 200032, China.

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概括

MDTips是一种新的多式联络融合系统,通过整合各种数据,准确地预测药物向相互作用 (DTI). 这种计算方法加速了药物发现和重新定位的努力.

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科学领域:

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

背景情况:

  • 药物向相互作用 (DTI) 的传统实验查是资源密集的.
  • 计算型DTI模型利用知识图,化学符号和基因组数据进行药物发现.
  • 需要一个统一的框架,用于多式联运数据融合在DTI预测.

研究的目的:

  • 开发一个多式融合DTI预测系统,整合不同的数据源.
  • 提高DTI预测模型的准确性和稳定性.
  • 通过先进的计算方法促进药物的重新用途和发现.

主要方法:

  • 开发了MDTips,一个融合知识图,基因表达特征和药物/标结构信息的系统.
  • 采用基于深度学习的编码器,包括Attentive FP和Transformer,用于特征提取.
  • 与传统化学描述符和最先进的预测模型相比,经过验证的性能.

主要成果:

  • 在DTI预测中,MDTips表现精确且强.
  • 多模式融合学习有效地纳入了来自不同数据方面的信息,提高了模型性能.
  • 深度学习编码器表现优于传统方法;MDTips超越了现有的最先进模型.
  • 成功反向选了6766种药物的候选标,有助于药物的重新用途.

结论:

  • MDTips为DTI预测提供了一种强大的多式联运方法.
  • 该系统集成多种数据的能力提高了其在药物发现管道中的实用性.
  • MDTips提供了一种有价值的工具,用于识别新型药物向关联,并促进药物的重新用途.