Simple QM9 properties for prediction I was looking into an article (doi: 10.1063/1.5058063) which presented a histogram of radius of gyration for samples of different configurations of alanine dipeptide. This property is fairly simple to calculate using 
$$
a_{\mathrm{Rg}}(x)=\sqrt{\frac{\sum_{p} m_{p}|| x_{p}-x_{\mathrm{COM}}||^{2}}{\sum_{p} m_{p}}}
$$
The sum considers all atoms $p = 1, \dots, P$ of the peptide, where $m_{p}$ and $x_{p}$ denote the mass and the coordinates of each atom, respectively. $x_{\mathrm{COM}}$ denotes the center of mass of the peptide.

In a different problem, I have a number of molecular graphs generated by a network trained using QM9 dataset and am looking for properties that are readily calculable for the newly generated molecular graphs using such packages as RDKit and OpenBable.

The properties in the QM9 dataset itself, like LUMO energy and energy of atomization, need DFT calculation, which is not something that I look forward to do. An example of a property of interest is QED, which can be easily calculated using RDKit package. But QED is more of interest in ZINC dataset.

I'd appreciate comments on any easy to compute property of interest to the community for QM9 dataset.
I found 3 properties:


The molecular weight (MolWt) 
Wildman-Crippen partition coefficient (LogP)
Topological Polar Surface Area (TPSA)