In comparison to BO method which required a sufficient size of the initial dataset together with objective function values, GA method only needs a small set of substances whose activities on the chosen target are confirmed, such as approved drugs, or lead compounds. The GA method was used on nine FDA-approved drugs as starting points to obtain 218 different molecules, whose predicted pIC 50 values ranged from 4.50 to 7.40. A brief overview of the results is given in Fig. as two-dimensional principal component analysis (PCA) using RDK7 fingerprints. To emphasize the impact of the learning rates, the generated molecules from the sorafenib exploration were visualized in Fig. , where molecules resulted from greater learning rates locate further to the initial point. The numbers of found molecules corresponding to each starting point are listed in Table . However, compared to other staring points, the numbers of molecules resulting from sunitinib, axitinib, and vandetanib (8, 33, and 11 molecules before removing duplicates, respectively) were remarkably lower. † 4 This may highlight the importance of placing starting points in the latent space, where they may fall into uncertain or uneven subspaces. In other words, changes in these starting points' latent vectors could lead to significant structural changes in the generated molecules, affecting their Tanimoto similarities with respect to their starters. Furthermore, as the learning rate increases, the latent vectors undergo stronger changes, according to the algorithm (Algorithm 1). Therefore, we applied constraints on Tanimoto similarity, and only a limited number of learning rates were considered in this study (Section 2.5).