Figure : Overview of the proposed approach. The PoE chemical language model efficiently navigates ultra-large virtual chemical spaces by combining a pre-trained prior model with fine-tuned expert and anti-expert models. The prior model is trained on a sparsely enumerated library of the target chemical space. The expert and anti-expert models are trained on desirable and undesirable compounds for each property of interest, respectively. During compound generation, the PoE model samples SMILES strings from the ensemble probability distribution modulated by the hyperparameters α i , which can be adjusted a posteriori.