We firstly aimed to understand the distribution of our datasets across the time and bioactivity domain, the results of which are shown in Fig. . For the public projects, the aSNN between α and γ are much higher (by around 0.1) than those between α and β. However, for the in-house projects, the aSNN between α and γ were mostly similar to, or lower than, values between α and β except for project C. . The validity for each target was over 98%. The uniqueness of generated compounds for the public dataset was relatively high, from 39.4% (GSK3) to 82.2% (DRD2), while the corresponding value for the in-house datasets was much lower, ranging from 15.1% (project F) to 50.9% (project E). The novelty of each target was over 70%