(1) its predictions are more efficient/accurate since the model distinctly separates new molecular representations according to their bioactivities (by reducing the distance metric between the same-labeled pair of data and increasing the distance between opposite-labeled pair of data), and (2) it automatically generates a vector-shaped representation from the molecular fingerprint and can be directly integrated with conventional dimension reduction methods. The final clusters were selected based on the best Silhouette score, which analyzes the distances of each data point to its cluster and neighboring clusters. In short, a higher Silhouette score indicates more distinct and separated clusters. We then identified which substructures or molecular functional groups played essential roles in determining the bioactivity of the molecules.