Figure : Overview of the workflow for de novo drug design described in this paper. Firstly, a DrugEx molecular generator is trained to generate compounds with activity for the A 2A R and a specified pharmacokinetic profile through reinforcement learning. It is trained to either maximize or minimize a specified pharmacokinetic property (CL, VDSS or FU). Then, the trained models are used to generate 10000 compounds each. Finally, QSPR models are used to predict A 2A R target affinity, VDSS and CL of the novel compounds, which form the input for a QSP model (adapted from. This model dynamically simulates the drug effect on the tumor microenvironment.