The QSAR model and molecular docking pipeline were applied to these molecules to calculate the predicted pIC 50 and binding affinity. The candidate score values were calculated separately for each optimization method. Top 10 of these molecules corresponding to BO and GA results based on the candidate score are presented in Fig. along with their assessment results. Inferred from the molecular docking results, most of these topranked molecules were found to form either hydrogen bonds or Van der Waals interactions with Cys919, Asp1046 or Glu885, which are crucial residues in inhibiting VEGFR-2. † 5 Notably, GA48, with a docking score of -10.3 kcal/mol and a predicted pIC 50 of 6.155 (Fig. ), formed a hydrogen bond with the backbone of Cys919 through the oxygen atom of its methoxy group, and hydrogen bonds with the side chain of Glu885 and Asp1046 (Fig. ). Similarity, BO23_pchembl_68_30_1000 and BO66_dual_100_2000 achieved promising docking scores of -11.7 kcal/mol and -11.5 kcal/mol, respectively. While they did not form hydrogen bonds with Cys919, interacting only through Van der Waals forces, they established new hydrogen bonds with the side chain of Cys1045 via the oxygen atom of their amide groups within the binding site. These interactions contribute to the rigidity of the potential compounds within the binding pocket. In terms of top-performing molecules in BO experiments, seven out of the top ten molecules came from runs that had their initial dataset cut off. In further analysis, the differences between the two types of objective functions, the predicted pIC 50 and the dual values, in BO were also taken into account. While the dual function values may not be limited in a range compared to the predicted pIC 50 , it could drive the optimization process to focus on generating molecules that are unfavourable hydrophobic, since the logP values are not upper bounded. For example, molecules from a certain run using dual values as the objective function are shown in Fig. . These molecules, while syntactically and grammatically correct within the dataset, do not correspond to viable chemical structures. They have chains of phenyl rings, contain a low amount or even absence of either nitrogen, oxygen or halogen atoms, and are highly hydrophobic. This phenomenon could also happen to GA method when optimization is done without constraints such as molecular similarity. Fortunately, the problem could be avoided by utilizing the initial dataset for BO, experimenting with different configurations, applying constraints or other assessments that can discern and reject chemically infeasible configurations. Therefore, a pIC 50 cut-off dataset and multiple settings were proposed for BO, and Tanimoto similarity was applied to GA. The LOLBO method were also applied to help limiting the search space to prevent over-exploration of areas that are unreliable. The candidate score could also counter this phenomenon, since unfavourable molecules could have either low predicted pIC 50 values or bad binding affinity. For instance, unfavourable molecules could have good binding affinity due to overflowed Van der Waals interactions, but low predicted pIC 50 values, resulting in poor candidate scores (Fig. ).