A novel application of 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 reaction generation approach to perform reaction generation with the Trans-VAE model in this study. To comprehend how exploratory and innovative the model is in reaction generation, we constructed the dataset by time-split. We applied the Michael addition reaction as the generation vehicle and took the reactions reported before a certain date as the training set and explored whether the model could generate reactions that were reported after the date. We took 2010 and 2015 as the time points for the splitting of the Michael addition reaction respectively. 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 additionally generated a large quantity of new chemically feasible Michael addition reactions, which also demonstrated the learnability of the Trans-VAE model for reaction rules. Our research provides a reference for future novel reaction discovery using deep learning.