The results presented above demonstrate the capability of the PoE chemical language model to effectively combine multiple expert and anti-expert models, allowing for multiconstraint compound generation within the target chemical space. A key advantage of the PoE chemical language model is its flexibility, as it does not require explicit knowledge of the chemical space or molecular properties of interest. While the present study focused on specific molecular properties that can be evaluated in silico and are easily interpretable, the PoE chemical language model can potentially be applied to any property that can be expressed as a binary label. Prospective applications to experimentally measured data, such as bioactivity assays or ADME profiling, represent crucial directions for future research. For these challenging scenarios, iterative approaches incorporating feedback from experimental evaluations may be necessary to progressively improve the quality of generated compounds.