具有知识蒸的异质采样子图神经网络,以增强双盲化合物-蛋白相互作用预测
Ying Xia1, Xiaoyong Pan1, Hong-Bin Shen1
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
Structure (London, England : 1993)
|March 6, 2024
概括
这项研究介绍了SgCPI,这是一种基于网络的新方法,用于预测化合物-蛋白相互作用 (CPI). SgCPI通过利用本地网络数据和知识蒸,有效地预测新分子的相互作用.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 准确的化合物-蛋白相互作用 (CPI) 预测对于药物开发中的虚拟查至关重要.
- 现有的基于网络的方法难以预测新型化合物和蛋白质的相互作用.
- 挑战在于将预测模型概括为看不见的分子实体.
研究的目的:
- 开发一种新的方法,SgCPI,用于可靠的CPI预测,特别是对于未见的化合物和蛋白质.
- 通过结合本地交互网络信息来提高预测准确性.
- 通过知识蒸技术来提高模型的概括性.
主要方法:
- SgCPI使用来自本地化合物-蛋白相互作用 (CPI) 网络的随机子图采样.
- 使用异质图形神经网络 (HGNN) 来嵌入采样子图的交互信息.
- SgCPI-KD通过使用知识蒸来估计未见分子的潜在邻居,扩展了SgCPI.
主要成果:
- 采样子图有效地转移知识,用于预测使用HGNN的看不见分子的相互作用.
- 知识蒸策略显著有利于双盲互动预测.
- SgCPI在识别新型化合物-蛋白相互作用方面表现得更好.
结论:
- 局域网信息和HGNN是用于未见的分子相互作用预测的强大工具.
- 知识蒸增强了模型预测新型化合物和蛋白质相互作用的能力.
- SgCPI为药物发现中大规模虚拟查提供了一个有希望的方法.
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