PK and PD are inherently linked and are therefore both important optimization criteria in drug discovery. In this proof-of-concept, we have demonstrated how we can capture trends in PK characteristics while simultaneously optimizing the potency of generated compounds through the reinforcement-learning generative drug design framework DrugEx. In addition, we have shown how the effect of different PK and potency optimization criteria may be understood through QSP modeling. For practical exploitation of this framework, the results should be experimentally validated. An important limitation of this work is the limited performance of the QSPR model for CL. In future work, we will refine this pipeline through uncertainty quantification and improve the efficacy simulations through PBPK modeling.