An alternative approach to overcoming this limitation is to leverage deep generative models that enable direct sampling of compounds. Various molecular representations, model architectures, and optimization methods have been explored to facilitate the generation of valid and novel compounds with desired properties. In particular, chemical language models, which utilize string representations of compounds (e.g., SMILES ), have shown promise in navigating chemical spaces. Recurrent neural networks, or more recently, transformer architectures, are typically employed to predict the next token in a sequence given the preceding tokens. These models are trained on the basis of maximum likelihood estimation and can subsequently be fine-tuned via transfer learning or reinforcement learning to generate compounds optimized for specific objectives.