Reinforcement Learning for Improving Chemical Reaction Performance.

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Abstract

Deep learning (DL) methods have gained notable prominence in predictive and generative tasks in molecular space. However, their application in chemical reactions remains grossly underutilized. Chemical reactions are intrinsically complex: typically involving multiple molecules besides bond-breaking/forming events. In reaction discovery, one aims to maximize yield and/or selectivity that depends on a number of factors, mostly centered on reacting partners and reaction conditions. Herein, we introduce RE-EXPLORE, a novel approach that integrates deep reinforcement learning (RL) with an RNN-based deep generative model to identify prospective new reactants/catalysts, whose yield/selectivity is estimated using a pretrained regressor. Three chemical databases (ChEMBL, ZINC, and COCONUT containing half a million to one million unlabeled molecules) are independently used for pretraining the generators to enrich them with valuable information from diverse chemical space. Standard RL methods are found to be insufficient, as learners tend to prioritize exploitation for immediate gains, resulting in repetitive generation of same/similar molecules. Our engineered reward function includes a Tanimoto-based uniqueness factor within the RL loop that improved the exploration of the environment and has helped accrue larger returns. Integration of a user-defined core fragment into the generated molecules facilitated learning of specific reaction types. Together, RE-EXPLORE can navigate the reaction space toward practically meaningful regions and offers notable improvements across the three distinct reaction types considered in this study. It identifies high-yielding substrates and highly enantioselective chiral catalysts. This RL-based approach has the potential to expedite reaction discovery and aid in the synthesis planning of important compounds, including drugs and pharmaceuticals.