As a related example, the strategy of molecular optimization using similarity was recently applied by Zhavoronkov and coworkers. They reported that deep learning enabled rapid identification of potent DDR1 kinase inhibitors. Walters and Murcko analyzed the Zhavoronkov et al. study and reported that the AI-generated compound B (Figure ) shared a common substructure with an already marketed multi-kinase inhibitor (Ponatinib, Figure ), which was indeed included in the training set. In some more detail, they ring-closed a benzamide carbonyl into an isoxazole moiety to yield an equipotent and unique compound. These two compounds are very similar with regards to shape and electrostatics, see Figure . Thus, Zhavoronkov's AI method successfully mimicked typical medicinal chemistry behavior, keeping certain parts fixed and making minor modifications to other. The compounds share a rather large common substructure. The hydrogen-bond acceptor and donor functionalities are visualized with electrostatic contours (red: negative, blue: positive). The ESP-Sim Tanimoto value is 0.81 for this pair.