Generative deep learning has become a promising method for chemistry and drug discovery . Generative models learn the pattern distribution of the input data and generate new data instances based on learned probabilities . Among the proposed generative frameworks that have been applied to de novo molecular design , chemical language models (CLMs) have gained particular attention because of their ability to generate focused virtual chemical libraries and bioactive compounds . CLMs are trained on string representations of molecules, e.g., simplified molecular input line entry systems (SMILES) strings (Fig. ) . CLMs for molecular design iteratively predict the next SMILES character using all the preceding portions of the SMILES string (Fig. ). In this process, CLMs learn the conditional probability of sampling any SMILES character based on the preceding characters. After CLM training, the model can be used for molecular construction. CLMs have been demonstrated to both learn the SMILES syntax and capture semantic features of the training molecules, such as physicochemical properties , bioactivity, and chemical synthesizability . A desirable feature of CLMs is their ability to function in low-data regimes , i.e., with limited training data (typically in the range of 5 to 40 molecules) . One of the most widely employed approaches for low-data model training is transfer learning . This method leverages previously acquired information on a related task for which more data are available ("pretraining") before training the CLM on a more specific limited dataset ("fine-tuning") .