Retrosynthetic Analysis with Generative Adversarial Network

Abstract

Recently, retrosynthetic analysis with machine learning and deep learning has been proposed actively. We focus on the method proposed by Coley et al. In this method, we first calculate similarities between the target product and products in the reaction database to find similar products. Next, we generate candidate reactions by modifying reactions of the similar targets. The method by Coley et al. is more accurate than other methods. However, its search space is limited because it is based on the matching with the existing reactions. In this presentation, we propose a method with GAN(Generative Adversarial Network) in order to expand the search space. The idea of the proposed method is to learn a generative model with GAN, generate reactants with the generative model, and then a reaction with reaction prediction. We got new reactants with the proposed method. Besides, the ratio of the correct reactions was higher than the previous methods. We continue the detailed verification of the search space.