In addition, we also compareed the predictive performance of property-based molecular descriptors and the SC_2CDK fingerprints. Table summarizes the Tg and Td prediction results of the LightGBM models. As can be seen, the ML model with 1D and 2D molecular descriptors has better Tg prediction performance than the corresponding ML model with fingerprints. Therefore, the 1D and 2D molecular descriptors provide more important information relevant for Tg prediction compared with fingerprints. However, information contained in property-based descriptors (molecular descriptors) and fragment-based descriptors (fingerprints) can complement each other. The LightGBM prediction results reveal the most relative features with pronounced effects on the predicted Tg and Td in the dataset. Fig. shows the feature importance for predictions of Tg and Td obtained with the LightGBM-based optimal models. Descriptions of the 10 most important features for Tg and Td prediction are shown in Table and Table . The 10 most important features for Tg prediction are maxwHBa, JGI5, JGI9, JGI4, hmax, JGI10, VE1_D, ETA_dBetaP, JGI6, and ETA_EtaP_F. The