Abstract
Amide coupling is an important reaction widely applied in medicinal chemistry. However, condition recommendation remains a challenging issue due to the broad condition space. Recently, accurate condition recommendation via machine learning has emerged as a novel and efficient method to find suitable conditions to achieve the desired transformations. Nonetheless, accurately predicting yields is challenging due to the complex relationships involved. Herein, we present our strategy to address this problem. Two steps were taken to ensure the quality of the dataset. First, we selected a diverse and representative set of substrates to capture a broad spectrum of substrate structures and reaction conditions using an unbiased machine-based sampling approach. Second, experiments were conducted using our in-house high-throughput experimentation (HTE) platform to minimize the influence of human factors. Additionally, we proposed an intermediate knowledge-embedded strategy to enhance the model's robustness. The performance of the model was first evaluated at three different levels-random split, partial substrate novelty, and full substrate novelty. All model metrics in these cases improved dramatically, achieving an R (2) of 0.89, MAE of 6.1%, and RMSE of 8.0% in the full substrate novelty test dataset. Moreover, the generalization of our strategy was assessed using external datasets from reported literature, delivering an R (2) of 0.71, MAE of 7%, and RMSE of 10%. Meanwhile, the model could recommend suitable conditions for some reactions to elevate the reaction yields. Besides, the model was able to identify which reaction in a reaction pair with a reactivity cliff had a higher yield. In summary, our research demonstrated the feasibility of achieving accurate yield predictions through the combination of HTE and embedding intermediate knowledge into the model. This approach also has the potential to facilitate other related machine learning tasks.