Despite the apparent success of deep learning models in predicting the molecular electronic properties as shown above, the chemical essence embedded in the complex neural networks is still generally not easy to interpret. Comparatively, the important descriptors adopted in RF models can be described by Gini importancethe number of a particular descriptor used to split a node, being weighted by the number of the corresponding samples. Higher Gini scores imply more presence of the descriptor determining the classification of samples. Pubchem FP416 denotes the presence of C=C fingerprint in the sample molecules, and that straightforwardly represents the existence of π-orbitals, resulting in increasing HOMO energy, between similar saturated and unsaturated molecular frameworks. SubFPC300 represents the count of 1,3tautomerizable conjugation, adding more subtle criteria for describing conjugated molecular structures. GATS1c denotes Geary autocorrelation weighted by atomic charges at topological distance of 1.