Images of the 2D generated molecule structures (Figure ), selected through diversity clustering, show that most generated structures are similar to high-affinity molecules from the A 2A R dataset. Depending on the generation scenario, distinct types of scaffolds are generated. For example, in the maximized VDSS scenario, triazolo-pyrimidine and purine derivatives are more common than in the minimized VDSS scenario. Here, we did not use selectivity for the A 2A R as optimization criteria, thus generated compounds may be similar to compounds with activity for other adenosine receptor subtypes. For example, compound 1 from the maximize A 2A R + minimize CL scenario, where the dataset corresponding dataset compound is also active for the A 2B R (pKi 6.7). Patterns in physicochemical properties of the generated compounds generally match what was observed in the score distribution and UMAPs, namely different distributions of physicochemical properties for the generated molecules in different scenarios. Furthermore, the scenario with maximized FU compared to only maximized affinity clearly shows a trend towards lower lipophilicity, lower molecular weight and higher fraction of sp 3 hybridized carbons. This trend is reflected in the PK dataset as well (Supplementary Figure ) in the difference between low and high FU compounds. This further confirms a trade-off between affinity for A 2A R and FU. Moreover, for the maximization of the CL scenario, a decrease in molecular weight and increase in the fraction of sp 3 hybridized carbons is noted, compared to the minimization scenario. This difference is also present in the low versus high CL compounds in the PK dataset (Supplementary Figure ). For the maximization of VDSS scenario a lower polar surface area and higher fraction of sp 3 hybridized carbons is found compared to the maximization; and again a similar difference is found in PK dataset (Supplementary Figure ).