A quantum chemical reaction prediction (QC-RP) method based on machine learning was developed to predict chemical products from given reactants. The descriptors contain atomic information in reactants such as charge, molecular structure, and atomic/molecular orbitals obtained by the quantum chemical calculations. The QC-RP method involves two procedures, namely, learning and prediction. The learning procedure constructs screening and ranking classifiers using 1625 polar and 95 radical reactions in a textbook of organic chemistry. In the prediction procedure, the screening classifier distinguishes reactive and unreactive atoms and the ranking one provides reactive atom pairs in ranking order. Numerical assessments confirmed the high accuracies both of the screening and ranking classifiers in the prediction procedures. Furthermore, an analysis on the classifiers unveiled important descriptors for the prediction.
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