After preprocessing, 914 active substances were grouped into 23 clusters using the Butina algorithm and a Tanimoto coefficient threshold of 0.35. The Silhouette coefficient, an indicator of clustering performance, was 0.14. The centroids of these clusters were selected for a training set, labeled as set A. To ensure the structural diversity of the selected centroids, a similarity matrix was recalculated for the 23 centroids and visualized as a heatmap chart (Fig. ). Data dimensionality was reduced to two using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) algorithms, and the clustering distribution was presented using the seaborn library (Fig. ). While the clustering results were generally satisfactory, with components of each cluster grouped together, there was some overlap between clusters, particularly those with fewer components.