As described above, machine learning-based molecular design has made great technical progress over the past few years. However, its practical deployment has not progressed as much as expected. One reason for this is owing to the difficulty in determining the synthetic routes to such designed molecules. Concurrently , with the growing attention to molecular design, significant progress has been made in computational methods for synthetic route design . Similar to the molecular design task, the general workflow for synthetic route design consists of forward and inverse problems. The goal of the forward problem is to derive a model that predicts the chemical structure of a synthetic product for a given set of reactant molecules. In contrast, in the inverse problem, inverse mapping of the forward model is explored to identify a set of reactants that produces a given desired product. Recent developments in deep learning technologies have significantly improved the accuracy of predicting reaction outcomes in organic synthesis. For example, if the structures of the reactants and products are treated as graphs, the reaction prediction task can be formulated as a graph transformation problem . Under the string representation of reactants and products according to the simplified molecular input line entry system (SMILES) chemical language , deep neural networks for sequence-to-sequence translation can be utilized to predict the SMILES string of a product from input reactants. For example, it was reported that the reaction prediction using Transformer, a well-known encoder-decoder architecture for machine translation, could successfully predict the chemical structures of synthetic products with over 90% accuracy . Furthermore, several methods have been developed to solve the inverse problem of such forward reaction prediction models. The objective is to identify a set of promising reactants from a list of commercially available compounds that can synthesize a desired product .