Purely data-driven modelling techniques have had a fundamental impact on the analysis of biological sequences. In particular, neural networks have been used extensively, with successful applications in for instance the prediction of secondary structure, aggregation propensities, and disorder. In contrast, the 3D structure of molecules has been modelled almost exclusively with carefully parameterised physical force fields, which are notoriously difficult to optimise from data. Recent developments in Machine Learning are changing this picture, making it possible to learn structure-sequence relationships directly from raw molecular structures. In this primer, we will briefly review these developments, and introduce the concept of convolutional neural networks, which form the basis for many of the current activities, including the work we will present as our main talk.