While preserving 95% of variance, PCA reduced the number of machine learning features to 17 components (Figure ). The most contribution in first principal component (PC) was attributed to quantum properties of 4 atoms fragments detected in parent molecules. Fragments built up of 8 and 7 atoms had biggest impact on second PC. Third component was composed mainly of properties of 2 atoms fragments. Figure shows a 3D plot of first, third and seventh components. Amongst first 7 PCs the most informative quantum properties were internal energy, enthalpy and free energy at 298.15K, polarizability, zero point vibrational energy, heat capacity, energy of HOMO, gap and dipole moment.