Post-reaction, automated work-up processes were employed to quench reactions and prepare samples for analysis. Finally, high-throughput detection methods, including UPLC, MS, NMR, were utilized to analyze reaction outcomes efficiently. Detailed protocols and specific conditions for each step of the HTE process are provided in Section S2 of SI. As a result, more than 47,000 yield data were collected except for those discard data, where overlapping of chromatography peak and difficulty in NMR analysis usually result in failure to obtain the corresponding yield data. This dataset was designed to be rich and diverse, providing a robust foundation for training machine learning models. The aim was to ensure that our HTE data would be sufficiently comprehensive to enable the models to understand and predict the yields of amide coupling reactions across the entire chemical space covered by our study.