Here, we consider the problem of slow computation of the deep neural network for the reaction prediction. In this study, we used Molecular Transformer developed by Schwaller et al. for the forward prediction of a single-step reaction. In our test, a single-step reaction prediction of m = 500 particles using Molecular Transformer required 25-40 s on average on a Linux server with an NVIDIA V100 GPU. When the number of steps was set as T = 5000, the total runtime of the reaction prediction exceeded 56 hours. We therefore used a computationally light surrogate model Y = d(S) that directly predicts property Y from a given set of reactants S without going through the reaction prediction to generate the product. As described later, the chemical structure of each reactant in S was converted into a binary vector of length 3239 by concatenating the Morgan fingerprint with radius 2 and bit length 1024, the MACCS keys, and the RDKit fingerprint. The intersection of the binary vectors for the two reactions in S was then taken to obtain a single-descriptor vector. From the United States Patent and Trademark Office (USPTO) open chemical reaction dataset , which contains 1.1M reactions, we selected a subset of 492k reactions that involve exactly two reactants. We then substituted the selected reactant pairs in Molecular Transformer to obtain their products and calculated their model properties Y using the property predictor h. The gradient boosting regressor was then trained to learn the mapping from S to Y using 80% of the 492k instances as the training set. For hyperparameter tuning, using 20 candidate values for the learning rate, 15 for the max depth, and 10 for the number of estimators, we performed 10-fold cross-validation looped within the training set and selected the optimized hyperparameters attaining the smallest mean absolute error (MAE). In the calculation of the importance weights in the SMC module, the surrogate Y (x t ) = d(S) was used instead of Y (x t ) = h • r(S). Samples drawn from the proposal distribution at each step of the SMC were handed over to the validation module with Molecular Transformer r and property predictor h for the exact calculation of their synthetic products and resulting properties.