基于图形的链接预测的药物发现.
Xiaolong Shi1, R Sundareswaran2, M Shanmugapriya3
1Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.
这项研究介绍了一种使用图形神经网络的计算框架,用于预测用于新治疗的植物化学蛋白相互作用. 这种方法为开发自然疗法的传统方法提供了一个可扩展,具有成本效益的替代方案.
科学领域:
- 计算生物学 计算生物学
- 神经科学是一个神经科学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 影响着全球数以百万计的人,给社会和神经系统带来了重大挑战.
- 目前对机制的理解尚不完整,这阻碍了有效的治疗开发.
- 受阿育吠陀启发的植物化学蛋白相互作用为治疗提供了一个有前途的途径,特别是在抗药性病例中.
研究的目的:
- 开发一个计算框架来预测与治疗相关的植物化学蛋白相互作用.
- 为此预测任务利用基于图形的方法和图形神经网络 (GNN).
- 探索自然,阿育吠陀启发的管理替代方案.
主要方法:
- 模拟的植物化学-蛋白质相互作用作为一个双部分图.
- 使用的图形卷积网络 (GCN),图形注意网络 (GAT) 和图形SAGE.
- 使用单跳封闭子图来完善预测性能.
主要成果:
- 性能最好的GNN模型实现了高精度 (0.9778),精度 (0.9574),F1得分 (0.9782) 和ROC-AUC (0.9994) 的高精度.
- 基于图形的计算方法在预测相互作用方面被证明是有效和可扩展的.
- 该框架显示了识别新型治疗候选者的潜力.
结论:
- 拟议的计算框架准确地预测了潜在治疗的植物化学蛋白相互作用.
- 图形神经网络为神经疾病中药物发现提供了强大的工具.
- 由阿育吠陀启发的植物化学物质显示出自然治疗的希望,通过计算建模验证.
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