To evaluate our concept, we chose six different conditions with various coupling reagents that are frequently used in the literature and have well-defined intermediates. For the condition selection, we performed a statistical analysis of the literature-reported amide coupling reactions curated from Reaxys. We identified the 25 most frequently used conditions, as shown in Table of the SI, and selected six for model development and assessment, as shown in Table . These conditions were chosen based on their prevalence in the dataset, ensuring that our HTE conditions were both representative and relevant to a wide range of amide coupling reactions. In our investigation of selected condition models, we followed the meticulous approach in preparing our dataset, ensuring it mirrored the model's rigor through three distinct datasets: random split, partial substrate novelty, and full substrate novelty. to train the selected condition models using similar machine learning and deep learning algorithms respectively as those employed for multi-condition models (Figure ). However, in this case, the reaction conditions were no longer concatenated with the reaction vector.