机器学习策略的反应开发:向低数据极限的方向.
Eunjae Shim1, Ambuj Tewari2,3, Tim Cernak1,4
1Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Journal of chemical information and modeling
|June 14, 2023
概括
机器学习可以使用有限的数据来预测有机反应. 转移学习和积极学习策略弥合了数据密集型模型和专家化学家用于反应开发的低数据方法之间的差距.
科学领域:
- 有机化学 有机化学
- 机器学习 机器学习
- 计算化学计算化学
背景情况:
- 机器学习模型越来越多地用于预测有机反应结果.
- 目前的模型需要大量的数据集,与使用有限数据的专家化学家不同.
- 在将机器学习应用于有机合成中低数据场景时存在差距.
研究的目的:
- 引入积极学习和转移学习策略.
- 将这些策略与有机合成中的低数据挑战联系起来.
- 突出未来化学转化发展的机会.
主要方法:
- 积极学习原则的审查.
- 转移学习原则的审查.
- 分析它们对有机合成数据限制的适用性.
主要成果:
- 积极和转移学习可以有效地运行有限的反应数据.
- 这些方法为弥合数据密集型ML和专家直觉之间的差距提供了一条途径.
- 确定了推动未来化学反应发展的潜力.
结论:
- 主动学习和转移学习对有机合成中的低数据机器学习具有前景.
- 这些策略可以增强新化学转换的发展.
- 进一步的研究可以利用这些方法来应对现实世界的合成挑战.
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