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Machine learning techniques paired with the availability of massive datasetsdramatically enhance our ability to explore the chemical compound space byproviding fast and accurate predictions of molecular properties. However,learning on large datasets is strongly limited by the availability ofcomputational resources and can be infeasible in some scenarios. Moreover, theinstances in the datasets may not yet be labelled and generating the labels canbe costly, as in the case of quantum chemistry computations. Thus, there is aneed to select small training subsets from large pools of unlabelled datapoints and to develop reliable ML methods that can effectively learn from smalltraining sets. This work focuses on predicting the molecules atomization energyin the QM9 dataset. We investigate the advantages of employing domainknowledge-based data sampling methods for an efficient training set selectioncombined with informed ML techniques. In particular, we show how maximizingmolecular diversity in the training set selection process increases therobustness of linear and nonlinear regression techniques such as kernel methodsand graph neural networks. We also check the reliability of the predictionsmade by the graph neural network with a model-agnostic explainer based on therate distortion explanation framework.

On the Interplay of Subset Selection and Informed Graph Neural Networks