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

Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability