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DMHGNN:双重多视图异构图神经网络框架用于药物向相互作用预测
Qiao Ning1, Yue Wang2, Yaomiao Zhao2
1The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, Jiangsu, China; Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China; Neusoft Education Technology Group, Dalian 116026, Liaoning, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, Jilin, China.
这项研究引入了一种新的双多视图异构图神经网络 (DMHGNN),用于预测药物向相互作用 (DTI). DMHGNN通过使用先进的图形神经网络技术全面分析药物-蛋白质对信息来提高DTI预测的准确性.
科学领域:
- 计算化学和化学信息学
- 生物信息学和计算生物学
- 人工智能在药物发现中的作用
背景情况:
- 准确的药物向相互作用 (DTI) 识别对于有效的药物发现至关重要.
- 传统的DTI预测实验方法耗时且昂贵.
- 现有的计算方法往往忽略了异质网络中的综合药物-蛋白质对 (DPP) 信息.
研究的目的:
- 提出一种新的计算框架,即双多视图异构图神经网络 (DMHGNN),用于改进DTI预测.
- 通过全面探索DPP信息来解决传统方法的局限性.
- 提高药物发现管道中DTI预测的准确性和效率.
主要方法:
- 开发DMHGNN,集成两个多视图异构图神经网络.
- 利用元路径和自动编码器来从蛋白质和药物相关的异质网络中学习.
- 使用多通道图形卷积网络 (GCNs) 来从药物-蛋白质对相似性网络 (拓学,语义学,协作图) 中学习.
主要成果:
- 基于元路径的图形编码器以注意力识别关键的子结构并学习多源邻近特征.
- 无声自编码器有效地从异质网络中学习药物和蛋白质特征.
- 在DTI预测实验中,DMHGNN与最先进的方法相比表现优越.
结论:
- DMHGNN为准确的药物向相互作用预测提供了强大而有效的框架.
- 拟议的方法显著推进了药物发现中的计算方法.
- 这一框架为加速识别潜在的候选药物提供了一个有希望的方向.
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