A lack of efficacy or safety is a major cause of drug failure. Drug efficacy is dependent on both pharmacokinetics (PK) and pharmacodynamics (PD). This makes the prediction of drug dose and PK a main concern during (model-based) drug development. Generative de novo drug design is used to efficiently explore the vast drug-like chemical space in early drug discovery. However, research in de novo drug design tends to focus on optimizing potency. Therefore, one of the key areas that needs to be addressed in generative artificial intelligence (AI) in drug discovery is the incorporation of PK and PD. To describe drug PK and PD, generally, PKPD and quantitative systems pharmacology (QSP) models can be used. A thorough mechanistic understanding of the biological system is required to understand the relationship between PK and PD. QSP models can describe the relationship between receptor activation and biomarkers for efficacy and toxicity. Consequently, the integration of PKPD and QSP modeling could play a role in bridging the gap between potency and efficacy in de novo drug design.