We applied gradient boosting regression to construct surrogate models that predict the two target properties from a set of two reactants without going through the prediction of synthetic products using Molecular Transformer. We randomly selected two pairs of 492K reactants from USPTO, generated their products using Molecular Transformer, and calculated their QED and logP. The resulting set of 394K samples was used to train the surrogate models for QED and logP. The R 2 values for predicting QED and logP for the 98K additional test samples were 0.004 and 0.172, respectively.