Note that the PCA embedding is used solely for selecting representative parent SMILES and for visualization purposes, not for downstream predictive tasks. Thus, a linear technique like PCA suffices. Although non-linear methods like t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) could have been explored, PCA is straightforward, interpretable, and computationally efficient, making it well-suited for visualizing high-dimensional data in a lower-dimensional space.