知识图信息瓶增强分子表示学习学习
Jiaxin Dai1, Dongmei Fu1, Zhongwei Qiu2
1Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
概括
本研究引入了知识图信息瓶 (KGIB) 框架,以改善分子表示学习 (MRL) 进行属性预测. KGIB有效地压缩知识,减少噪音并提高分子任务的准确性.
科学领域:
- 计算化学计算化学
- 机器学习 机器学习
- 化学信息学 化学信息学
背景情况:
- 分子表示学习 (MRL) 对于预测分子性质至关重要.
- 基于图表的MRL方法利用分子拓学.
- 整合知识图 (KG) 提高了MRL,但可能引入噪音.
研究的目的:
- 为增强分子表示学习开发一种新的框架.
- 解决来自外部知识来源的冗余和杂信息问题.
- 提高在MRL中使用的知识的最低充分性.
主要方法:
- 提出了一个知识图信息瓶 (KGIB) 框架.
- 构建了一个分子知识图 (MKG),详细说明了分子-功能组-元素关系.
- 实现了一个知识压缩模块,以识别最小足够的知识子图.
- 开发了一个知识对齐模块,以捕捉拓学-语义依赖关系.
主要成果:
- 在KGIB框架中,KGIB在10个现实任务中表现出卓越的性能.
- 在六个任务上表现优于最先进的基线.
- 在剩余的四个任务中取得了极具竞争力的结果.
- 有效地将不相关的知识噪声降到最低,同时保留重要信息.
结论:
- 拟议的KGIB框架显著增强了分子表示学习.
- 有效的知识压缩和调整是提高MRL性能的关键.
- 通过优化知识集成,KGIB为分子性质预测提供了一种卓越的方法.
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