To achieve controlled compound generation within a desired chemical space, we adopt the PoE approach in chemical language models. Given a prior model p(x t | x <t ), pre-trained on a sparsely enumerated library of the target chemical space, and a set of expert and antiexpert models p + i (x t | x <t ), p - i (x t | x <t ) i , fine-tuned using annotated datasets for specific properties, the ensemble probability distribution of the PoE chemical language model is formulated as: