Predicting reaction performance in C–N cross-coupling using machine learning

doi:10.1126/science.aar5169

Predicting reaction performance in C–N cross-coupling using machine learning

2018-04-13

0

PRE

AI

DOI：10.1126/science.aar5169

原文链接

原文求助

摘要

En 中文

A guide for catalyst choice in the forestChemists often discover reactions by applying catalysts to a series of simple compounds. Tweaking those reactions to tolerate more structural complexity in pharmaceutical research is time-consuming. Ahnemanet al.report that machine learning can help. Using a high-throughput data set, they trained a random forest algorithm to predict which specific palladium catalysts would best tolerate isoxazoles (cyclic structures with an N–O bond) during C–N bond formation. The predictions also helped to guide analysis of the catalyst inhibition mechanism.Science, this issue p.186

Predicting reaction performance in C–N cross-coupling using machine learning

摘要

AI总结

期刊

机构

引用论文