克莱尔:一种基于学习的对比性预测器,用于EC化学反应的数量
Zishuo Zeng1, Jin Guo2, Jiao Jin2
1Synceres Biosciences Co. Ltd., Shenzhen, 518100, China. zengzishuo@synceres.com.
Journal of cheminformatics
|January 8, 2025
概括
使用对比学习的新框架CLAIRE准确地预测化学反应的酶委员会 (EC) 数. 这种方法克服了数据的局限性,大大改善了计算机辅助合成规划和相关应用.
科学领域:
- 生物化学 生物化学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 酶反应预测对于计算机辅助合成规划至关重要.
- 传统的机器学习方法在这个领域扎着数据稀缺和阶级不平衡.
研究的目的:
- 引入CLAIRE (基于对比学习的反应EC注释),这是一个新的框架,用于预测化学反应的EC数.
- 在酶反应预测中解决数据稀缺和阶级失衡的挑战.
主要方法:
- 利用对比学习来改善模型培训.
- 使用预先训练的语言基于模型的反应嵌入.
- 采用数据增强技术来提高数据集的稳定性.
主要成果:
- 在测试组 (n=18,816) 上达到0.861的加权平均F1得分,在独立酵母代谢模型数据集 (n=1,040) 上达到0.911的加权平均F1得分.
- 在各自的数据集上表现明显优于最先进模型的3.65倍和1.18倍.
- 在预测酶反应的EC数量方面表现出高准确度.
结论:
- 克莱尔为酶反应EC数预测提供了强大的解决方案,克服了常见的机器学习局限性.
- 该框架显示了对回复合成规划,药物命运预测和合成生物学中的应用的希望.
- 克莱尔代表了计算机辅助合成规划工具的重大进步.
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