一个双分支图形神经网络架构用于药物标结合亲和力预测
Khushnood Abbas1, Chen Hao2, Dong Shi3
1School of Computer Science and Technology, Zhoukou Normal University, Henan, China. khushnood.abbas@zknu.edu.cn.
一个新的双分支图形神经网络 (GNN) 通过改善分子表示来增强人工智能药物发现. 这种人工智能方法加快了候选人选和药物重新定位,超过了现有的模型.
科学领域:
- 化学信息学 化学信息学
- 计算机化药物发现技术
- 人工智能在医学中的应用
背景情况:
- 传统的药物发现是耗时和资源密集的.
- 计算策略,包括图形神经网络 (GNN),提供加速的目标识别和候选优先级.
- 整合FDA批准的药物库可以增强计算方法.
研究的目的:
- 引入一种新的双分支GNN架构,用于增强分子表示学习.
- 提高候选药物查和优先级的准确性和稳定性.
- 为化学信息学任务建立一个新的基准.
主要方法:
- 开发了一个双分支的GNN,结合了图形卷积神经网络 (GCN),GraphSage和跳跃知识模块.
- 联合编码的分子图形结构拓和功能属性为丰富的嵌入.
- 在戴维斯和KIBA数据集上对45个最先进的基线进行了评估.
主要成果:
- 拟议的GNN模型显示了与GCN模型相比的定量改进.
- 实现了平均平方误差 (MSE) 的减少,从35.24降至33.98.
- 展示了更高的皮尔森 (76.49与76.19) 和协和指数 (85.41与84.41) 的优异表现.
结论:
- 双分支GNN为分子候选查提供了卓越的准确性和稳定性.
- 该模型为化学信息学任务建立了一个新的参考点.
- 一个COVID-19药物重用案例研究确定了潜在的抗病毒药物,包括Imunovir和Remdesivir.
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