预测反应条件:一个数据驱动的视角
Matthew Ball1,2, Dragos Horvath2, Thierry Kogej1
1Molecular AI, Discovery Sciences RD, AstraZeneca 431 83 Gothenburg Sweden.
Chemical science
|September 12, 2025
概括
机器学习模型可以预测化学反应条件,但在数据质量和表示方面面临挑战. 使用基于图表的新型输入来进行木-米亚乌拉反应,提高了超出简单人气基线的预测准确性.
科学领域:
- 合成化学 合成化学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 优化反应条件对于高效,可持续和可扩展的化学合成至关重要.
- 机器学习 (ML) 在计算机辅助合成规划 (CASP) 中预测反应条件方面表现有前途.
- 当前的ML模型在数据质量,稀疏性,反应表示和评估方面扎,往往无法超越简单的基线.
研究的目的:
- 批判性地审查用于反应条件预测的最新ML技术.
- 确定解决化学反应建模关键挑战的创新.
- 为了证明反应表示对模型性能的影响.
主要方法:
- 对用于反应条件预测的ML技术进行批判性审查.
- 使用美国专利局 (USPTO) 数据对异芳香的苏子 - 迈拉反应的案例研究.
- 凝结反应图 (CGR) 表示作为模型输入的应用.
主要成果:
- 确定了关键的创新,以应对反应条件预测的ML方面的挑战.
- 证明基于CGR的输入显著提高了苏子-米亚乌拉反应的预测能力.
- 显示了改进的模型性能,超过了文学衍生的人气基线.
结论:
- 反应表示对于改善CASP中的ML模型至关重要.
- CGR为表示反应提供了一个强大的替代方案,提高了预测准确度.
- 未来的工作应该集中在数据质量缓解和先进的建模策略上.
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