We further applied Taylor-Butina clustering to generate a representative set of molecules for detailed analysis. Taylor-Butina clustering is an algorithm that uses exclusion spheres at a specified Jaccard distance threshold. We performed a benchmark clustering using RDKit Butina clustering module and a Jaccard distance ranging from 0 to 1, ultimately identifying an optimal distance threshold of 0.4. This threshold resulted in a balance between the number of clusters, singletons, and intra-cluster similarities. The centroids of each cluster, along with singletons, were included in our final dataset (referred to as 'centroids', totaling of 406 molecules) for further calculations and analysis. Fig. , shows the intra-cluster similarities for the first 10 clusters, along with the Bemis-Murcko scaffold for each cluster's centroid.