自主监督的对比分子表示学习用化学合成知识图学习
Jiancong Xie1, Yi Wang1, Jiahua Rao1
1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.
Journal of chemical information and modeling
|March 14, 2024
概括
通过从化学反应中学习分子表征,ReaKE增强了药物发现. 这种反应知识嵌入框架提高了化学反应任务的概括性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 药物发现 药物发现
背景情况:
- 自主监督的分子表示学习加速了药物开发.
- 现有的方法在有限的反应数据和对反应特定任务的概括方面扎.
研究的目的:
- 提出ReaKE,一个新的反应知识嵌入框架.
- 为了有效地将化学反应的先前信息纳入分子表示中.
- 为了提高化学反应相关任务的概括性.
主要方法:
- 制定化学反应作为知识图,其中包含反应物,产物和反应规则.
- 构建了一个化学合成知识图.
- 在分子和反应层面开发了新的对比学习.
主要成果:
- ReaKE捕获与反应相关的功能组信息.
- 与最先进的方法相比,证明了有效性.
- 在反应分类,产品预测和产量预测方面取得了强的表现.
结论:
- ReaKE有效地整合了化学反应知识.
- 该框架增强了化学任务的分子表示学习.
- 在加速药物发现和开发方面,ReaKE具有显著的潜力.
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