Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods

Recently, supervised machine learning has been ascending in providing new predictive approaches for chemical, biological, and materials sciences applications. In this Perspective, we focus on the interplay of machine learning methods with the chemically motivated descriptors and the size and type of datasets needed for molecular property prediction. Using nuclear magnetic resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data are abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning methods drawn from deep learning, random forests, or kernel methods.