Several studies have explored the integration of PK or absorption, distribution, metabolism & elimination (ADME) in early drug discovery computational methods. For example, (posthoc) filtering for ADME properties has been applied in virtual screening and de novo drug design. Moreover, Horne et al. proposed simultaneous optimization of central nervous system (CNS) ADME properties, toxicity and potency through a generative machine learning method. A direct approach to the integration of PK into a generative AI framework was to generate novel compounds with increased CNS exposure through the use of physiologicallybased pharmacokinetic (PBPK) modeling and quantitative structure-property relationship (QSPR) models for the estimation of PK parameters as input to the generative model in a reinforcement learning framework. Yet, the sole focus here was optimizing PK, rather than the combination of PK and PD. Another tool, the commercial AI-driven drug design platform (AIDD™) integrates ADME and PK properties directly into generative drug design. This tool allows users to integrate ADME and potency predictions into an evolutionary algorithm for de novo drug design. However, they do not link the predicted potency and PK properties of generated compounds to the PD and this tool is not open-source which limits usability by academics.