In this study, we demonstrated a fine balance between chemical property predictivity and chemical knowledge interpretability using linear, random forest, deep learning models. We applied a systematic approach to extract a few hundred critical knowledge-based molecular descriptors, from 17k-plus descriptors generated by PaDEL, using the linear and random forest models for predicting E H , E L , and ΔE HL of the organic molecules in QM9 dataset. Such a simple approach only requires the valence bond connectivity of the molecules, being encrypted in SMILES files, and does not require molecular structure optimizations, commonly being achieved by empirical potentials or quantum chemistry calculations. The predictivity provided by the current linear and random forest models can achieve MAEs at 4-6 times of chemical accuracy while the qualitative chemical interpretation, being represented by the identification of the leading descriptors, can be extracted from these numerical formulations.