Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit
Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit
复制标题
反应开发的机器学习策略：迈向低数据极限
DOI：
10.1021/acs.jcim.3c00577
复制
发表时间：
2023
影响因子：
5.6
通讯作者：
Zimmerman, Paul M.
中科院分区：
文献类型：
--
作者：
Shim, Eunjae;Tewari, Ambuj;Cernak, Tim;Zimmerman, Paul M.
机器学习模型越来越多地被用于预测有机化学反应的结果。大量的反应数据被用于训练这些模型，这与专家化学家如何通过利用少量相关转化的信息来发现和开发新反应形成鲜明对比。迁移学习和主动学习是两种可以在低数据情况下运行的策略，这可能有助于填补这一空白，并促进机器学习的使用，以应对有机合成中的现实挑战。本展望介绍了主动学习和迁移学习，并将它们与进一步研究的潜在机会和方向联系起来，特别是在化学转化的前景发展领域。
Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.