The most common data augmentation strategy has been applied to Simplified Input Molecular Line System (SMILES ) strings. SMILES strings are one of the most common ways to represent a molecular structure for deep learning. They encode two-dimensional aspects of the molecular structure (i.e., atom connectivity and type, and bond type) and, optionally, stereochemistry in the form of a string, by traversing the molecular graph and annotating the encountered atoms and bonds with predefined characters (Fig. ). Any heavy (nonhydrogen) atom can be used as a starting point, and, therefore, one molecule can have multiple valid and different SMILES strings. where two-dimensional molecular information is converted into a string format) and molecular graphs (where nodes and edges represent atoms and bonds, respectively). c. Two key tasks in drug discovery are molecular property prediction (whereby a property like bioactivity is predicted from the molecular structure) and de novo design (whereby structures with desirable properties are generated from scratch).