In the present work we have demonstrated how machine learning approaches can very effectively augment properties predicted by accurate but expensive DFT calculations. A selection of ML models could effectively predict structural and electronic properties of van der Waals heterostructures. The use of property labelled materials fragments as descriptors for the monolayers proved to be effective, yielding relatively high and practically useful prediction accuracies for the interlayer spacing and bandgap. Fast prediction of these properties should also improve the synthesis bottleneck by predicting which materials are likely to function effectively and which are not worth synthesizing.