HiCLR:知识诱导的等级对比学习与逆合成预测产生反应基础模型
Jialu Wu1, Yiheng Zhu2, Xiaorui Wang3,1
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
JACS Au
|August 1, 2025
概括
化学反应的新框架HiCLR使用知识导向学习来创建更好的反应指纹. 这种方法改善了化学中的AI,用于诸如合成规划和属性预测等任务.
科学领域:
- 化学领域的人工智能
- 机器学习用于化学反应.
背景情况:
- 目前化学的深度学习模型通常仅依赖结构信息,这限制了它们对各种合成相关任务的有效性.
- 结构上相似的反应可以表现出截然不同的特性,突显出需要更复杂的反应表征.
研究的目的:
- 开发一个基本的反应模型,通过结合领域知识来克服现有方法的局限性.
- 创建化学上有意义和广泛适用的反应指纹,使用知识诱导的层次对比学习框架.
主要方法:
- 提出了HiCLR (反应等级对比学习),一个引入关系感应偏差的框架.
- 实施了一种预训练方案,将回归合成预测和对比损失相结合,用于同时处理基于生成和基于理解的任务.
主要成果:
- HiCLR成功地将反应空间组织成与化学知识一致的等级语义集群.
- 在多个与合成相关的任务中实现了最先进的性能,包括反应分类,条件建议,产量预测和合成规划.
- 在分子性质预测方面表现出有效性,展示了广泛的适用性.
结论:
- HiCLR是化学反应的第一个基础模型,能够广泛应用于与合成相关的任务.
- 将域名知识纳入神经网络学习显著增强AI驱动的化学进步.
- 该框架提供了具有化学意义的反应指纹,提高了在各种化学建模任务中的性能和适用性.
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