机器学习有时可以简单地捕捉文学流行趋势:异环苏-米亚拉合的案例研究
Wiktor Beker1,2, Rafał Roszak1,2, Agnieszka Wołos1,2
1Allchemy, Inc., Highland, Indiana 46322, United States.
机器学习 (ML) 模型难以预测最佳的苏子基-米亚拉合条件,即使有大量文献数据. 这表明需要系统生成的标准化数据集来训练化学反应模型.
科学领域:
- 合成化学
- 机器学习
- 计算化学
背景情况:
- 机器学习 (ML) 越来越多地使用文献数据来预测化学反应.
- 精确的预测模型对于优化合成化学反应至关重要.
- 假设大数据集可以构建稳健的模型.
研究的目的:
- 评估ML模型在预测木-米亚拉合的最佳反应条件中的有效性.
- 确定精选的文献数据是否足以准确地预测ML驱动的化学反应性.
- 在合成化学中确定当前ML方法的局限性.
主要方法:
- 采用了超过1万种苏子-米亚乌拉合的文献数据库.
- 应用了各种ML模型,包括前和图形卷积神经网络.
- 测试了不同的化学表征 (指纹,描述符,潜伏表征).
- 模型的性能与基于天真频率的分配进行了比较.
主要成果:
- ML模型未能为最佳反应条件 (溶剂,) 提供有意义的预测.
- 不同ML架构或化学表现的性能没有显著改善.
- 基于常见条件的随机猜测并没有显著改善ML的预测.
结论:
- 仅凭大量精选的文献数据就不足以训练合成化学中的准确ML模型.
- 文献报告中的偏见 (例如主观偏好,试剂可用性) 和缺乏负面数据阻碍了ML的性能.
- 系统生成可靠的标准化数据集对于在化学反应性预测中推进ML至关重要.
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