Shim Eunjae, Tewari Ambuj, Cernak Tim, Zimmerman Paul M
Department of Chemistry, University of Michigan Ann Arbor MI USA
Department of Statistics, University of Michigan Ann Arbor MI USA.
Chem Sci. 2025 Feb 3;16(9):4109-4118. doi: 10.1039/d4sc06728b. eCollection 2025 Feb 26.
Pinpointing effective reaction conditions can be challenging, even for reactions with significant precedent. Herein, models that rank reaction conditions are introduced as a conceptually new means for prioritizing experiments, distinct from the mainstream approach of yield regression. Specifically, label ranking, which operates using input features only from substrates, will be shown to better generalize to new substrates than prior models. Evaluation on practical reaction condition selection scenarios - choosing from either 4 or 18 conditions and datasets with or without missing reactions - demonstrates label ranking's utility. Ranking aggregation through Borda's method and relative simplicity are key features of label ranking to achieve consistent high performance.
确定有效的反应条件可能具有挑战性，即使对于有大量先例的反应也是如此。在此，引入对反应条件进行排序的模型，作为一种概念上全新的实验优先级确定方法，有别于产率回归的主流方法。具体而言，仅使用来自底物的输入特征进行操作的标签排序，将被证明比先前的模型更能推广到新的底物。在实际反应条件选择场景中的评估——从4种或18种条件中选择，以及有或没有缺失反应的数据集——证明了标签排序的实用性。通过博尔达方法进行排序汇总以及相对简单性是标签排序实现一致高性能的关键特征。
