We further assessed the coverage of chemical space and the diversity of the generated molecules by analyzing their atom scaffolds (Bemis-Murcko scaffolds) . We examined the five most frequent scaffolds of sampled molecules before, i.e., using the pretrained chemical language model, and during transfer learning (Fig. ). As a measure of scaffold diversity, we determined the Shannon entropy scaled by the number of investigated scaffolds (scaled Shannon entropy, SSE, Eq. 2). SSE quantitatively reflects the structural diversity of a given set of scaffolds. Here, SSE = 1 indicates maximum diversity, whereas SSE = 0 indicates full conservation of a single molecular scaffold. During the transfer learning process, the number of molecules containing one of the five most frequent scaffolds increased, whereas their diversity decreased in terms of SSE. When assessing the whole population, the number of unique scaffolds decreased by approximately 50% during transfer learning. The fraction of singletons, i.e., scaffolds occurring only once in a population, also decreased (Table , Supporting Information). This result shows that transfer learning with the structurally conserved natural products 1-5 (Fig. ) led to the de novo design of a structurally focused compound collection that predominantly contains the chemical scaffold of the transfer learning set.