The product SMILES strings were then converted into extended connectivity fingerprints (1024-bit ECFP) 39 with a radius of 2, serving as numerical representations of the molecular structures. To manage the high-dimensional nature of the Morgan fingerprints and facilitate analysis, we employed Principal Component Analysis (PCA), an unsupervised learning technique, to reduce the dimensionality of the data while preserving the variance inherent in the molecular descriptors. The data was reduced to a two-dimensional space primarily for visualization purposes and to observe the overall distribution of amide coupling reactions within the chemical space. It is important to note that this dimensionality reduction was not used for clustering but rather for visualization. While PCA was chosen for this purpose, other dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t- SNE) or uniform manifold approximation and projection (UMAP), 42 which were often better at preserving distances in high-dimensional spaces were also attempted as alternatives for PCA. However, t-SNE and UMAP visualizations both exhibit globular structure, which indicates neither t-SNE nor UMAP can provide a uniform distribution like PCA does as shown in Figures S16 and S17 of the Supporting Information (SI).