Suppr超能文献

对“使用机器学习预测 C-N 交叉偶联反应性能”一文的评论的回复。

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

机构信息

Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.

Modeling and Informatics, Merck & Co., Inc., Kenilworth, NJ 07033, USA.

出版信息

Science. 2018 Nov 16;362(6416). doi: 10.1126/science.aat8763.

Abstract

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.

摘要

我们证明了在原始论文中描述的化学特征模型与 Chuang 和 Keiser 引入的不可泛化模型不同。此外,在样本外预测中,化学特征模型的性能明显优于这些模型,证明了使用化学特征化的方法是合理的,机器学习模型可以从数据集中提取有意义的模式,如最初所描述的那样。

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