Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?

The Art of Organic Synthesis in the Age of Automation

(2018) Anat Milo   ISRAEL JOURNAL OF CHEMISTRY

Fast and accurate prediction of the regioselectivity of electrophilic aromatic substitution reactions

(2018) Jimmy C. Kromann et al.   Chemical Science

Predictive Model for Site-Selective Aryl and Heteroaryl C–H Functionalization via Organic Photoredox Catalysis

(2017) Kaila A. Margrey et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Prediction of Organic Reaction Outcomes Using Machine Learning

(2017) Connor W. Coley et al.   ACS Central Science

Optimizing Chemical Reactions with Deep Reinforcement Learning

(2017) Zhenpeng Zhou et al.   ACS Central Science

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

(2017) G. Skoraczyński et al.   Scientific Reports

Computation and Experiment: A Powerful Combination to Understand and Predict Reactivities

(2016) Theresa Sperger et al.   ACCOUNTS OF CHEMICAL RESEARCH

Computing organic stereoselectivity – from concepts to quantitative calculations and predictions

(2016) Qian Peng et al.   CHEMICAL SOCIETY REVIEWS

Neural Networks for the Prediction of Organic Chemistry Reactions

(2016) Jennifer N. Wei et al.   ACS Central Science

Conversion of amides to esters by the nickel-catalysed activation of amide C–N bonds

(2015) Liana Hie et al.   NATURE

Using IR vibrations to quantitatively describe and predict site-selectivity in multivariate Rh-catalyzed C–H functionalization

(2015) Elizabeth N. Bess et al.   Chemical Science

Contemporary screening approaches to reaction discovery and development

(2014) Karl D. Collins et al.   Nature Chemistry