使用正面和未标记的机器学习预测醇氧化同合中的基质活性
Takafumi Nishii1, Kaname Ichizawa1, Haruka Nagano1
1Department of Chemistry and Life Science, Yokohama National University, Hodogaya-Ku, Yokohama 240-8501, Japan.
ACS omega
|November 3, 2025
概括
一个新的机器学习模型预测了氧化同对基质的反应性. 这种积极和未标记的 (PU) 学习方法在不需要负数据的情况下实现了高准确性,从而实现了具有成本效益的分子合成.
科学领域:
- 有机化学 有机化学
- 机器学习 机器学习
- 计算化学的计算化学
背景情况:
- 预测基质反应性对于高效的有机合成至关重要.
- 的氧化同是一种具有广泛应用的关键反应.
- 传统的机器学习模型通常需要广泛的标记数据,包括负面示例.
研究的目的:
- 开发和验证一个积极和未标记的 (PU) 学习模型,用于预测氧化同合中的基质反应性.
- 用不同的描述器集和实验数据来评估模型的预测性能.
- 为了证明PU学习在化学合成预测中的传统方法上的优势.
主要方法:
- 训练PU学习模型来预测基质反应性.
- 使用两个描述符集用于模型验证:28维描述符和扩展连接指纹.
- 使用实验数据进行参数调整,以优化模型性能.
- 使用未标记的数据评估预测准确性,并与经过积极和消极数据训练的模型进行比较.
主要成果:
- 优化的PU学习模型在各种反应条件下,在现有实验数据上实现了出色的预测准确性.
- 使用30种未标记数据的预测与实验结果对83.3-86.7%的基质有很好的相关性.
- 与与正和负反应率数据一起训练的模型相比,PU学习模型表现出更高的准确性.
结论:
- PU学习是一种有效的策略,用于预测氧化同合中的基质反应性.
- 开发的模型提供了高的预测能力,而不需要负的实验数据.
- 这种方法通过利用易于获得的文献数据,促进了具有成本效益的分子合成.
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