Chemical language models (CLMs) are now a well-established method for de novo molecule generation. Seminal methods evidenced their ability to learn to generate valid and novel SMILES strings when trained on a corpus of example SMILES , use transfer learning (a.k.a. fine-tuning) to condition generation to a particular chemical sub-space , or reinforcement learning (RL) to condition generation in order to maximize an arbitrary objective according to a scoring function(s) that evaluates de novo chemistry . Since seminal works, there has been much research and proposed improvements, such as using diversity filters to penalize excessive exploitation , use of experience replay (and augmented variations ), or exploration and modification of RL algorithms to accelerate learning. In light of alternative and more complex model architectures, CLMs remain either 1 st or 2 nd most performant according to a variety of benchmarks and are the most commonly published deep learning model for de novo molecule generation . Furthermore, they have undergone experimental validation evidencing their ability to generate bioactive molecules de novo in the context of drug design . Overall CLMs are of increasing utility and importance to augmenting and automating the drug design process.