基于多个过器的签名异质图卷积网络,用于预测激活/抑制药物向相互作用.
Ming Chen1, Haike Li1, Yunhan Pan1
1College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
Methods (San Diego, Calif.)
|May 17, 2025
概括
这项研究引入了一种新的图形卷积网络,用于预测药物向相互作用机制,区分激活和抑制. 该模型通过分析签名网络上的药物向相互作用来增强药物发现.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 网络科学 网络科学
背景情况:
- 药物向相互作用 (DTI) 对药物发现至关重要,但预测它们的激活/抑制机制仍然具有挑战性.
- 传统的DTI分析实验室方法耗时且昂贵.
- 现有的DTI预测研究往往忽视了激活或抑制的具体机制.
研究的目的:
- 开发一个计算模型来预测药物向相互作用的激活和抑制机制.
- 利用签署的异质网络来代表和分析DTI和药物之间的关系.
- 引入一种基于多个过器的新型签名异质图形卷积网络 (MFSHGCN),用于增强DTI机制预测.
主要方法:
- 在签名异质网络上建模 DTI,将交互分类为签名链接.
- 建立基于共同目标的药物连贯性/不连贯性的签名药物链接.
- 提出基于多个过器的签名异质图形卷积网络 (MFSHGCN),使用双过器从正边和负边进行光谱信息融合.
- 开发一个端到端的框架来预测DTI中的激活和抑制.
主要成果:
- 该MFSHGCN模型有效预测药物向相互作用机制,优于现有方法.
- 药物对连贯性/不连贯性和多过器系统的整合显著改善了预测指标.
- 该模型即使没有广泛的节点信息或药物/目标对相互作用,也能显示出高预测准确度.
- 关于乳腺和肺癌的案例研究证实了该模型的实际可行性和有效性.
结论:
- 拟议的MFSHGCN框架提供了一种强大的计算方法,用于预测药物向相互作用机制.
- 这种方法可以通过提供对DTI的机制性见解来加速药物发现管道.
- 该模型能够使用有限的节点信息,使其在生物信息学和药物开发中广泛适用.
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