A Novel Application of a Generation Model in Foreseeing 'Future' Reactions

Deep learning is widely used in chemistry and can rival human chemists in certain scenarios. Inspired by molecule generation in new drug discovery, we present a deep-learning-based approach to reaction generation with the Trans-VAE model. To examine how exploratory and innovative the model is in reaction generation, we constructed the data set by time splitting. We used the Michael addition reaction as a generation vehicle and took these reactions reported before a certain date as the training set and explored whether the model could generate reactions that were reported after that date. We took 2010 and 2015 as time points for splitting the reported Michael addition reaction; among the generated reactions, 911 and 487 reactions were applied in the experiments after the respective split time points, accounting for 12.75% and 16.29% of all reported reactions after each time point. The generated results were in line with expectations and a large number of new, chemically feasible, Michael addition reactions were generated, which further demonstrated the ability of the Trans-VAE model to learn reaction rules. Our research provides a reference for the future discovery of novel reactions by using deep learning.