Deep learning (DL) offers potential to accelerate drug design by efficiently traversing chemical space, defined as the set of all possible biologically relevant molecules, and estimated to be on the order of 10 23 to 10 60 . The task is challenging as candidate drug molecules must satisfy a multi-parameter optimization (MPO) objective where parallel optimization of the individual objectives can be difficult. Thus, a brute-force molecular search approach is often infeasible. DL-augmented molecular design combined with computational oracles to approximate physico-chemical properties has enabled candidate drug molecules to be designed in an accelerated manner. DL-based approaches to the drug design task include deep generative models with reinforcement learning (RL) , learning a molecular latent space , and genetic algorithms , which generate molecular ideas as Simplified Molecular-Input Line-Entry System (SMILES) strings or molecular graphs .