Machine learning (ML), especially deep learning, has become a powerful tool for accurately predicting molecular properties and chemical reactivity. Fast and accurate property prediction is of great interest to pharmaceutical and materials research, where it can significantly accelerate the discovery of novel drugs and materials. Deep learning models fundamentally seek to learn a mapping from the molecular input to the target property by optimizing its parameters. The choice of molecular representation is critical for capturing the useful relationship between the molecule and the target property to achieve generalizable models.