  Nowadays the development of new functional materials/chemical compounds using
machine learning (ML) techniques is a hot topic and includes several crucial
steps, one of which is the choice of chemical structure representation.
Classical approach of rigorous feature engineering in ML typically improves the
performance of the predictive model, but at the same time, it narrows down the
scope of applicability and decreases the physical interpretability of predicted
results. In this study, we present graph convolutional neural networks (GCNN)
as an architecture that allows to successfully predict the properties of
compounds from diverse domains of chemical space, using a minimal set of
meaningful descriptors. The applicability of GCNN models has been demonstrated
by a wide range of chemical domain-specific properties. Their performance is
comparable to state-of-the-art techniques; however, this architecture exempts
from the need to carry out precise feature engineering.
