From the trained RF models, the importance of each feature in performing prediction was extracted. Molecular fragments that have high contributions to the oscillator strength of a molecule were identified from the ECFP. Also, critical molecular substructures defined in the MACCS keys and molecular descriptors were identified. To analyze the feature importance, a fingerprint with a longer diameter (ECFP6) and more vector bits (16384) were used to search for huger and more diverse fragments. The four bits with the highest feature importance were selected to discover important fragments that induce high oscillator strength. All fragments corresponding to each selected bit were obtained by the reverse process of the generation. All molecules that include these fragments were then searched from the PubChemQC database.