In this work, we simulated the effect of either maximization or minimization of a PK property through reinforcement learning with a QSP model with a fixed dosing regimen to evaluate how in vivo efficacy of generated molecules in different scenarios compared. However, the results may have changed if we allowed the dosing to be dynamic with constraints on the amount and frequency to correct for drug exposure. A clinically relevant question could be what the minimum required dose of a drug would be to achieve a predefined efficacy level. Then, the objectives in learning could be to both optimize the drug potency and drug exposure. Another option would be to build on the pipeline proposed by Chen et al. where (among other applications) an optimal molecular space is defined through the determination of the PK driver for efficacy using virtual enumeration of a QSP model.