Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
Open Access
- 27 April 2018
- journal article
- editorial
- Published by Wiley in Angewandte Chemie-International Edition
- Vol. 57 (24), 6978-6980
- https://doi.org/10.1002/anie.201803562
Abstract
Machines learn chemistry: An artificial intelligence algorithm has learned to predict the outcomes of C−N coupling reactions from a few thousand nanomole‐scale experiments. This Highlight discusses this work in the context of other state‐of‐the‐art approaches for predicting the yields of organic reactions and explains the significance of the results.Keywords
Funding Information
- H2020 European Research Council (682002)
This publication has 13 references indexed in Scilit:
- Fast and accurate prediction of the regioselectivity of electrophilic aromatic substitution reactionsChemical Science, 2017
- Predictive Model for Site-Selective Aryl and Heteroaryl C–H Functionalization via Organic Photoredox CatalysisJournal of the American Chemical Society, 2017
- Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?Scientific Reports, 2017
- Prediction of Organic Reaction Outcomes Using Machine LearningACS Central Science, 2017
- Neural Networks for the Prediction of Organic Chemistry ReactionsACS Central Science, 2016
- Computing organic stereoselectivity – from concepts to quantitative calculations and predictionsChemical Society Reviews, 2016
- Computation and Experiment: A Powerful Combination to Understand and Predict ReactivitiesAccounts of Chemical Research, 2016
- Conversion of amides to esters by the nickel-catalysed activation of amide C–N bondsNature, 2015
- Using IR vibrations to quantitatively describe and predict site-selectivity in multivariate Rh-catalyzed C–H functionalizationChemical Science, 2015
- Contemporary screening approaches to reaction discovery and developmentNature Chemistry, 2014