Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, 02-097, Warsaw, Poland.
DARPA Make-It Program & the Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland.
Sci Rep. 2017 Jun 15;7(1):3582. doi: 10.1038/s41598-017-02303-0.
As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors.
随着机器学习/人工智能算法在击败国际象棋大师之后，最近又在击败围棋冠军，人们开始产生兴趣并希望这些算法在帮助化学家预测有机反应结果方面同样有用。然而，本文表明，机器学习在不同类型化学中的化学反应性问题上的适用性仍然有限 - 特别是，目前可用的化学描述符，基本数学定理对反应产率和时间的预测精度施加了上限。要提高机器学习方法的性能，需要开发全新的化学描述符。
