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Recently supervised machine learning has been ascending in providing newpredictive approaches for chemical, biological and materials sciencesapplications. In this Perspective we focus on the interplay of machine learningalgorithm with the chemically motivated descriptors and the size and type ofdata sets needed for molecular property prediction. Using Nuclear MagneticResonance chemical shift prediction as an example, we demonstrate that successis predicated on the choice of feature extracted or real-space representationsof chemical structures, whether the molecular property data is abundant and/orexperimentally or computationally derived, and how these together willinfluence the correct choice of popular machine learning algorithms drawn fromdeep learning, random forests, or kernel methods.

Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms