In this section, we describe the development and assessment of our multi-condition models for predicting amide coupling reaction yields. As shown in Figure , we employed several machine learning algorithms, including XGBoost, Support Vector Machine (SVM), Random Forest, and AutoGluon, 50 utilizing 1024-bit ECFP fingerprints as descriptors. Additionally, we explored the use of advanced deep learning textual methods such as yield-BERT 14 and T5-Chem, which leverage reaction SMILES strings for yield prediction. First, we generated 1024-bit ECFP descriptors from the SMILES strings of reactants and products. These ECFP descriptors, with 1024 bits and a radius of 2, capture the structural features of the molecules involved in the reactions. These fingerprints of substrates and product of a reaction were concatenated to create a vector of 3072 or point-wise added to keep size of 1024, as the reaction fingerprint. Each of the different reaction conditions was encoded in a unique integer (1-95). We then used these descriptors to train the XGBoost, SVM, Random Forest, and AutoGluon 50 models. Each model was fine-tuned to optimize hyperparameters, ensuring the best performance for yield prediction. AutoGluon, a robust ensemble model, combines the strengths of various machine learning algorithms to improve predictive performance and model robustness. Figure uses the modelling workflow of AutoGluon to exemplify the machine learning approaches.