In the following workflow, we evaluated the excitation energy, drug-likeness, and SAscore of these molecules, as illustrated in Figure . We utilized the Coulomb matrix of molecules as the molecular descriptor in our prediction model. Support Vector Regression (SVR) served as the predictive machine learning model. We methodically compared various sets of parameters for SVR, encompassing different kernels (rbf, sigmoid and quantum), regularization parameter (from 0.01 to 80), and kernel coefficient (auto, scale, 0.8, 0.84, and 2.3). Based on the Mean Squared Error (MSE) of both the training and test sets, our predictive model opted for the rbf kernel, a regularization parameter of 80, and a kernel coefficient of 0.84. Before we used our SVR of excitation energies to predict the value of generative molecules, the deepchem was used to calculate the Coulomb matrix of generative modelcules. Prior to utilizing our SVR for predicting the excitation energies of generative molecules, we employed DeepChem to compute Analyzing the density distribution plots of excitation energies depicted in Fig. , we observe a range spanning from 5 to 11 eV, with the majority concentrated between 8 and 9 eV. This distribution closely aligns with that of the training samples, indicating UVGPT's success in learning QSAR from molecular data and facilitating molecular design.