Recommending Multiple Reaction Conditions Using Two-Stage Deep Neural Networks

Abstract

The temperatures, prices of reagent and solvent and the yields influence the feasibility of the synthetic pathway. Therefore, predicting reaction condition is an important topic when designing a profitable synthetic pathway. For a single-step reaction, there are sometimes more than one suitable reaction contexts. Different reaction conditions result in different reaction rate and selectivity, and the design should comply with the requirement of the chemists. Providing diverse alternatives could help design the more economic synthesis pathway. However, recent literature has only tried to predict one best reaction condition. To improve this situation, we construct a twostage listwise ranking model to recommend multiple reaction conditions, and the ranking metrics are based on the yield level. The model is trained on the dataset consisting of ten representative types of reaction exported from Reaxys, and it recommends the reaction conditions with the top- 20 mean average precision (MAP) equal to 0.2723. The MAE of the temperature prediction is 11.1 °C. Besides, we used t-SNE to reduce the dimensionality of the embeddings and found that the model implicitly learns the pattern of reaction classification when predicting the reaction conditions.