Selectivity in organocatalysis—From qualitative to quantitative predictive models

Recent advances in both experimental and computational techniques pose an exciting time for chemistry. Computational tools traditionally used to interpret trends have now evolved into predictive models able guide the design of novel catalysts. This review discusses evolution these models, as well challenges future avenues field organocatalysis. Through representative examples we demonstrate how traditional physical organic chemistry combination with machine learning provide a powerful approach achieve deeper understanding alongside greater power. article is categorized under: Structure Mechanism > Reaction Mechanisms Catalysis Electronic Theory Density Functional Data Science Artificial Intelligence/Machine Learning