A model that exhibits this property can be used in novel applications for instance to estimate the density of the near-neighborhood chemical space. This was previously not possible. In particular, we could demonstrate an approximately exhaustive enumeration of the local, precedented chemical space for a molecule of interest. We showed that neighborhood sizes computed using this method scale in a similar way as the GDB-12 database. From a drug design perspective, the presented model will be most influential at a later drug development stage, where the focus changes to exploring the chemical space around a lead compound. Using our model, we can cheaply but extensively explore the chemical space without being limited to available building blocks or previously synthesized molecules, and without needed to conduct similarity searches against massive virtual libraries. In a second step, a property evaluation of the generated molecules would follow to select the most promising compounds to follow up in a target-appropriate manner. Evaluation of such proprieties is target and project dependent and not the focus of the current work. However, we emphasize that the models developed here can be practically useful idea generators for drug discovery applicable out of the box, and here we provide a simple example of using them for the structure-based virtual lead optimization of Phosphoinositide-dependent kinase-1 (PDK1) inhibitors in the Appendix E.