关于"使用机器学习预测C-N交叉合的反应性能"的评论
Kangway V Chuang1, Michael J Keiser2
1Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases, and Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA.
概括
评估了用于预测C-N交叉合反应产量的机器学习模型. 该研究发现实验设计不足以验证模型,未能通过经典机器学习控制.
科学领域:
- 化学学
- 机器学习
- 数据科学
背景情况:
- 预测化学反应的结果对于合成化学至关重要.
- 机器学习 (ML) 提供了预测反应产量的可能性.
- 准确的模型验证对于化学中可靠的ML应用至关重要.
研究的目的:
- 评估ML模型在预测C-N交叉合反应产量的有效性.
- 使用化学描述符与随机特征评估ML模型的有效性.
主要方法:
- 使用原子,电子和振动描述器作为输入特征的ML模型的应用.
- 使用了追溯和前性测试场景.
- 在化学特征和随机值特征上训练的模型性能比较.
主要成果:
- 实验设计无法充分区分化学特征和随机特征训练的模型.
- 这些ML模型未能通过传统的验证.
- 化学描述物的预测能力尚未确定.
结论:
- 目前的实验设计不足以验证反应产量预测的ML模型.
- 为了可靠地评估ML模型在化学中的性能,需要进一步细化实验设计.
- 这项研究强调了严格验证在化学研究中的重要性.
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