可解释性
计算机科学
胺化
人工智能
Boosting(机器学习)
机器学习
特征(语言学)
还原胺化
化学
催化作用
有机化学
语言学
哲学
作者
Jing Dong,Lichao Peng,Xiaohui Yang,Zelin Zhang,Puyu Zhang
摘要
Buchwald-Hartwig amination reaction catalyzed by palladium plays an important role in drug synthesis. In the last few years, machine learning-assisted strategies emerged and quickly gained attention. In this article, an importance and relevance-based integrated feature screening method is proposed to effectively filter high-dimensional feature descriptor data. Then, a regularized machine learning boosting tree model, eXtreme Gradient Boosting, is introduced to intelligently predict reaction performance in multidimensional chemistry space. Furthermore, convergence, interpretability, generalization, and the internal association between reaction conditions and yields are excavated, which provides intelligent assistance for the optimal design of coupling reaction system and evaluating the reaction conditions. Compared with recently published results, the proposed method requires fewer feature descriptors, takes less time, and achieves more accurate prediction accuracy.
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