NHGNN-DTA:一个节点适应性的混合图形神经网络,用于可解释的药物标结合亲缘关系预测
Haohuai He1, Guanxing Chen1, Calvin Yu-Chian Chen1,2,3
1Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China.
一个新的混合神经网络NHGNN-DTA通过集成序列和基于图的方法来增强药物向亲和力预测. 这种可解释的模型实现了最先进的结果,即使在药物发现的冷启动场景中也提供了强大的性能.
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.
- 机器学习 机器学习
背景情况:
- 药物标亲和力 (DTA) 预测对于药物发现至关重要.
- 目前用于DTA预测的机器学习方法要么使用序列信息,要么使用结构信息,在特征提取和信息交互方面存在局限.
研究的目的:
- 提出NHGNN-DTA,一个节点适应性的混合神经网络,用于可解释的DTA预测.
- 结合基于序列和基于图形的方法的优势,以改善DTA预测.
主要方法:
- 开发了一个节点适应性混合神经网络 (NHGNN-DTA).
- 采用多头自我注意机制来实现模型的可解释性.
- 集成的自适应特征表示和图表级信息交互.
主要成果:
- 在戴维斯 (MSE 0.196) 和KIBA (0.124) 数据集上实现了最先进的性能.
- 与基线方法相比,在冷启动场景中表现出优越的稳定性和有效性.
- 通过模型可解释性为药物发现提供了新的探索性见解.
结论:
- NHGNN-DTA为DTA预测提供了一种强大而可解释的方法.
- 该模型处理看不见的输入的能力使其在药物发现方面具有价值.
- 关于Omicron变种的案例研究突出了在传染病中药物重新利用的潜力.
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