Recently there have been growing evidence that properties computed using quantum mechanics (QM) methods can offer additional information about molecules that are difficult to learn from the molecular graph representation itself without many examples . By relying on more abstract similarities between products, QM methods are powerful tools and can be used to extract reactivity trends for organic reactions . For example, QM methods can be used to identify reactive atomic sites via local reactivity descriptors . Local reactivity descriptors, such as the Fukui functions, indicate how the electron density of a given molecule responds to an attack of another and have been applied to identify which sites are most suitable for electrophilic or nucleophilic attack . A set of such local chemical descriptors can therefore carry essential information about the chemical reactivity. In this paper, we test the hypothesis that we can reduce the skewed performance of expansion policy networks by introducing semi-empirical QM properties. The QM data is embedded in the model through graph convolution. While we find that this approach improved performance we show that the improvement comes from the graph convolutional molecular representation instead of the fingerprint representation used originally. Secondly, we show that the skewed performance can be decreased further by using oversampling of rare templates.