The MAEs of E H , E L , and ΔE HL predictions can be reduced to generally match the level of chemical accuracy if the complex deep learning model -SchNET is adopted. Nonetheless, the success of SchNET model builds upon the collection of physical-meaningful molecular structures (Cartesian coordinates), being typically optimized by quantum chemistry calculations. With replacing the molecular Cartesian coordinates by bond-step matrixes, MAEs of SchNET(bs) models are about 2-times of chemical accuracy in comparison with those of SchNET(3D) cases. Such a moderate demotion of model predictivity may be worthwhile compensated by the reduced workload of dataset preparation. This SchNET(bs) approach is finally recommended to couple with chemical graph generators for the virtual screening of new organic molecules containing novel properties.