Predicting the Feasibility of Copper (I)-catalyzed Alkyne-Azide Cycloaddition Reactions Using Recurrent Neural Network with a Self-Attention Mechanism

The copper (I)-catalyzed alkyne-azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of CuAAC reaction is not satisfactory as expected, and in order to improve its performance, we developed a recurrent neural network (RNN) model to predict its feasibility. First, we designed and synthesized a structurally diverse library of 700 compounds with the CuAAC reaction to obtain experimental data. Then, using reaction SMILES as input, we generated a bidirectional long-short term memory with a self-attention mechanism (BiLSTM-SA) model. Our best prediction model has total accuracy of 80%. With the self-attention mechanism, adverse substructures responsible for negative reactions were recognized and derived as quantitative descriptors. DFT investigations were conducted to provide evidence for the correlation between bromo-α-C hybrid types and the success rate of the reaction. Quantitative descriptors combined wit...