MESPool:分子边缘收缩聚合用于层次分子表示学习和属性预测
Fanding Xu1, Zhiwei Yang2, Lizhuo Wang1
1School of Life Science and Technology, Xi'an Jiaotong University, 710049 Shaanxi, China.
Briefings in bioinformatics
|December 4, 2023
概括
我们开发了分子边缘收缩聚合 (MESPool),这是一个新的图形神经网络聚合方法. MESPool有效地识别了用于属性预测的关键分子结构,同时保持了可解释性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 药物发现 药物发现
背景情况:
- 确定与任务相关的分子结构对于准确的属性预测至关重要.
- 图形神经网络 (GNN) 使用图形聚合用于层次分子表示.
- 现有的聚合方法往往会丢失节点信息或图形连接,阻碍子结构识别和可解释性.
研究的目的:
- 为分子图表引入一种新的图形聚合方法.
- 改进连续的,与任务相关的子结构的识别.
- 为了提高图形神经网络模型在分子性质预测中的解释性.
主要方法:
- 拟议的分子边缘收缩聚合 (MESPool),是一种基于边缘的聚合技术.
- MESPool保留了基本的化学键 (边缘),同时缩小了功能组内的不那么关键的化学键.
- 该方法允许在不破坏原始分子图连接的情况下搜索关键结构.
主要成果:
- 在各种基准中,MESPool在与已建立的聚合方法相比表现优越.
- 该方法成功地确定了关键的分子亚结构.
- 在数据集上验证了MESPool的解释性,包括一个COVID-19药物数据集.
结论:
- MESPool在图形聚合中为分子性质预测提供了显著的进步.
- 该方法增强了识别关键分子结构的能力,并提高了模型的解释性.
- MESPool在药物发现和材料科学领域的应用方面表现有前途.
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