Selectivity

Pathology

Organic chemistry

Feature (linguistics)

Reactivity (psychology)

Construct (python library)

On the fly

Politics

Gene

Operating system

Machine learning

Catalysis

Philosophy

Political science

Artificial intelligence

Chemistry

Programming language

Transformation (genetics)

Alternative medicine

Representation (politics)

Molecule

Computer science

Linguistics

Quantum chemical

Biochemistry

Medicine

Chemical Science

Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors